16.7.4.2 Human Detection, People Detection, Pedestrians, Locating

Jeong, M., Ko, B.C., Nam, J.Y.,
Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights,
CirSysVideo(27), No. 6, June 2017, pp. 1368-1380.
IEEE DOI 1706
Cameras, Feature extraction, Finite impulse response filters, Image color analysis, Roads, Support vector machines, Vehicles, Cascade random forest (CaRF), Keimyung University (KMU) pedestrian data set, far-infrared (FIR) image, sudden pedestrian crossing (SPC), virtual reference line BibRef

Li, Q., Wang, H., Yan, Y., Li, B., Chen, C.W.,
Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection,
ITS(18), No. 6, June 2017, pp. 1549-1558.
IEEE DOI 1706
Computational efficiency, Decision trees, Detectors, Feature extraction, Intelligent transportation systems, Quantization (signal), Training, Local co-occurrence, partial least squares, pedestrian detection BibRef

Pak, J.M., Ahn, C.K., Shmaliy, Y.S., Shi, P., Lim, M.T.,
Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering,
HMS(47), No. 3, June 2017, pp. 332-342.
IEEE DOI 1706
Atmospheric measurements, Finite impulse response filters, Noise measurement, Particle measurements, Receivers, Robustness, Composite particle/finite impulse response (FIR) filter (CPFF), human localization, particle, filter, (PF) BibRef

Bilal, M.[Muhammad],
Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features,
IET-CV(11), No. 5, August 2017, pp. 350-357.
DOI Link 1707
BibRef

Bak, S.[Slawomir], San-Biagio, M.[Marco], Kumar, R., Murino, V.[Vittorio], Brémond, F.[Francois],
Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition,
SMCS(47), No. 9, September 2017, pp. 2538-2549.
IEEE DOI 1708
Computer vision, Correlation, Covariance matrices, Feature extraction, Manifolds, Market research, Standards, Brownian descriptor, covariance descriptor, pedestrian detection, reidentification BibRef

Bilal, M., Khan, A., Karim Khan, M.U., Kyung, C.M.,
A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems,
CirSysVideo(27), No. 10, October 2017, pp. 2260-2273.
IEEE DOI 1710
object detection, pedestrians, support vector machines, video surveillance, histogram of oriented gradients, linear support vector machine, BibRef

Li, F.[Fuliang], Zhang, R.[Ronghui], You, F.[Feng],
Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment,
IET-IPR(11), No. 10, October 2017, pp. 833-840.
DOI Link 1710
BibRef

Cheng, R.Z.[Ru-Zhong], Zhang, Y.J.[Yong-Jun], Wang, G.P.[Guo-Ping], Zhao, Y.[Yong], Khusravsho, R.[Rahmatulloev],
Haar-Like Multi-Granularity Texture Features for Pedestrian Detection,
IJIG(17), No. 04, 2017, pp. 1750023.
DOI Link 1711
BibRef

Coniglio, C.[Christophe], Meurie, C.[Cyril], Lézoray, O.[Olivier], Berbineau, M.[Marion],
People silhouette extraction from people detection bounding boxes in images,
PRL(93), No. 1, 2017, pp. 182-191.
Elsevier DOI 1706
People detection BibRef

Tang, S.[Suhua], Obana, S.[Sadao],
Improving performance of pedestrian positioning by using vehicular communication signals,
IET-ITS(12), No. 5, June 2018, pp. 366-374.
DOI Link 1805
BibRef

You, M., Zhang, Y., Shen, C., Zhang, X.,
An Extended Filtered Channel Framework for Pedestrian Detection,
ITS(19), No. 5, May 2018, pp. 1640-1651.
IEEE DOI 1805
Convolution, Deformable models, Feature extraction, Image color analysis, Kernel, Nonhomogeneous media, Semantics, filtered channel features BibRef

Osayamwen, F.[Festus], Tapamo, J.R.[Jules-Raymond],
Improved eigenspectrum regularisation for human activity recognition,
IJCVR(8), No. 4, 2018, pp. 435-454.
DOI Link 1808
BibRef

Toudjeu, I.T.[Ignace Tchangou], Tapamo, J.R.[Jules Raymond],
Slope Pattern Spectra for Human Action Recognition,
ICIAR18(381-389).
Springer DOI 1807
BibRef

Brits, A.M.[Alessio M.], Tapamo, J.R.[Jules R.],
A Shape and Energy Based Approach to Vertical People Separation in Video Surveillance,
ISVC09(II: 345-356).
Springer DOI 0911
BibRef

Hattori, H.[Hironori], Lee, N.[Namhoon], Boddeti, V.N.[Vishnu Naresh], Beainy, F.[Fares], Kitani, K.M.[Kris M.], Kanade, T.[Takeo],
Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator for Static Video Surveillance,
IJCV(126), No. 9, September 2018, pp. 1027-1044.
Springer DOI 1809
BibRef

Bartoli, F.[Federico], Lisanti, G.[Giuseppe], Karaman, S.[Svebor], del Bimbo, A.[Alberto],
Scene-dependent proposals for efficient person detection,
PR(87), 2019, pp. 170-178.
Elsevier DOI 1812
Person detection, Scene-dependent proposals, Gaussian mixture model, Scene modelling BibRef

Savelonas, M.A.[Michalis A.], Pratikakis, I.[Ioannis], Theoharis, T.[Theoharis], Thanellas, G.[Georgios], Abad, F.[Frédéric], Bendahan, R.[Rémy],
Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data,
CVIU(171), 2018, pp. 1-9.
Elsevier DOI 1812
Local shape descriptors, Fisher encoding, LIDAR, Pedestrian recognition BibRef

Pfeifer, L.[Lienhard],
Shearlet Features for Pedestrian Detection,
JMIV(61), No. 3, March 2019, pp. 292-309.
WWW Link. 1903
BibRef

Ji, Z.[Zhong], He, E.[Erlu], Wang, H.[Haoran], Yang, A.P.[Ai-Ping],
Image-attribute reciprocally guided attention network for pedestrian attribute recognition,
PRL(120), 2019, pp. 89-95.
Elsevier DOI 1904
Pedestrian attribute, Attention network, Intelligent surveillance BibRef

Bastian, B.T.[Blossom Treesa], Jiji, C.V.[C. Victor],
Pedestrian detection using first- and second-order aggregate channel features,
MultInfoRetr(8), No. 2, June 2019, pp. 127-133.
Springer DOI 1906
BibRef
Earlier:
Aggregated Channel Features with Optimum Parameters for Pedestrian Detection,
PReMI17(155-161).
Springer DOI 1711
BibRef

Shen, J., Zuo, X., Zhu, L., Li, J., Yang, W., Ling, H.,
Pedestrian Proposal and Refining Based on the Shared Pixel Differential Feature,
ITS(20), No. 6, June 2019, pp. 2085-2095.
IEEE DOI 1906
Proposals, Feature extraction, Detectors, Image color analysis, Pipelines, Boosting, Pedestrian proposal, directional radius pooling BibRef

Demiröz, B.E.[Baris Evrim], Salah, A.A.[Albert Ali], Bastanlar, Y.L.[Ya-Lin], Akarun, L.[Lale],
Affordable person detection in omnidirectional cameras using radial integral channel features,
MVA(30), No. 4, June 2019, pp. 645-655.
Springer DOI 1906
BibRef

Kiaee, N.[Nadia], Hashemizadeh, E.[Elham], Zarrinpanjeh, N.[Nima],
Using GLCM features in Haar wavelet transformed space for moving object classification,
IET-ITS(13), No. 7, July 2019, pp. 1148-1153.
DOI Link 1906
Car and pedestrian from their side-view in a video sequence. grey-level co-occurrence matrix in Haar wavelet space. BibRef

Braun, M., Krebs, S., Flohr, F.[Fabian], Gavrila, D.M.[Dariu M.],
EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes,
PAMI(41), No. 8, August 2019, pp. 1844-1861.
IEEE DOI 1907
Proposals, Benchmark testing, Object detection, Feature extraction, Urban areas, Deep learning, Training, Object detection, benchmarking BibRef

Kooij, J.F.P.[Julian F.P.], Flohr, F.[Fabian], Pool, E.A.I.[Ewoud A.I.], Gavrila, D.M.[Dariu M.],
Context-Based Path Prediction for Targets with Switching Dynamics,
IJCV(127), No. 3, March 2019, pp. 239-262.
Springer DOI 1903
Objects have multiple dynamic modes. BibRef

Yao, L.[Li], Wang, B.F.[Bo-Fan],
Pedestrian detection framework based on magnetic regional regression,
IET-IPR(13), No. 9, 18 July 2019, pp. 1431-1436.
DOI Link 1907
BibRef

Shen, J., Zuo, X., Yang, W., Prokhorov, D., Mei, X., Ling, H.,
Differential Features for Pedestrian Detection: A Taylor Series Perspective,
ITS(20), No. 8, August 2019, pp. 2913-2922.
IEEE DOI 1908
Feature extraction, Transforms, Taylor series, Graphics processing units, Task analysis, Robustness, Detectors, integral channel feature BibRef

Tan, Z., Yang, Y., Wan, J., Hang, H., Guo, G., Li, S.Z.,
Attention-Based Pedestrian Attribute Analysis,
IP(28), No. 12, December 2019, pp. 6126-6140.
IEEE DOI 1909
Feature extraction, Task analysis, Deep learning, Image recognition, Aggregates, Semantics, pedestrian parsing BibRef

Liu, W.[Wei], Liao, S.C.[Sheng-Cai], Hu, W.D.[Wei-Dong],
Efficient Single-Stage Pedestrian Detector by Asymptotic Localization Fitting and Multi-Scale Context Encoding,
IP(29), No. , 2020, pp. 1413-1425.
IEEE DOI 1911
Detectors, Proposals, Object detection, Head, Feature extraction, Benchmark testing, Fitting, Pedestrian detection, asymptotic localization fitting BibRef

Liu, W.[Wei], Liao, S.C.[Sheng-Cai], Hu, W.D.[Wei-Dong], Liang, X.Z.[Xue-Zhi], Chen, X.[Xiao],
Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting,
ECCV18(XIV: 643-659).
Springer DOI 1810
BibRef

Galiyawala, H.[Hiren], Raval, M.S.[Mehul S.], Dave, S.[Shivansh],
Visual appearance based person retrieval in unconstrained environment videos,
IVC(92), 2019, pp. 103816.
Elsevier DOI 1912
Linear filtering, Person retrieval, Semantic description, Soft biometrics, Video surveillance BibRef

Zhang, S., Xie, Y., Wan, J., Xia, H., Li, S.Z., Guo, G.,
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild,
MultMed(22), No. 2, February 2020, pp. 380-393.
IEEE DOI 2001
Benchmark testing, Detectors, Training, Urban areas, Cameras, Task analysis, Deep learning, Pedestrian detection, dataset, high density BibRef

Chandrasekar, K.S.[Karnam Silpaja], Geetha, P.[Planisamy],
Highly efficient neoteric histogram-entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection,
IET-IPR(14), No. 2, February 2020, pp. 354-365.
DOI Link 2001
BibRef

Rasouli, A., Tsotsos, J.K.,
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice,
ITS(21), No. 3, March 2020, pp. 900-918.
IEEE DOI 2003
Survey, Pedestrian Detection. Autonomous vehicles, Roads, Cameras, Automobiles, Observers, pedestrian behavior, traffic interaction, survey BibRef

Ye, Q.X.[Qi-Xiang], Zhang, T.L.[Tian-Liang], Ke, W.[Wei],
Progressive Latent Models for Self-Learning Scene-Specific Pedestrian Detectors,
ITS(21), No. 4, April 2020, pp. 1415-1426.
IEEE DOI 2004
Detectors, Proposals, Optimization, Feature extraction, Cameras, Stability analysis, Deep learning, Pedestrian detection, difference of convex BibRef

Ye, Q.X.[Qi-Xiang], Zhang, T.L.[Tian-Liang], Ke, W.[Wei], Qiu, Q., Chen, J., Sapiro, G., Zhang, B.,
Self-Learning Scene-Specific Pedestrian Detectors Using a Progressive Latent Model,
CVPR17(2057-2066)
IEEE DOI 1711
Detectors, Linear programming, Optimization, Proposals, Stability analysis, Supervised learning, Training BibRef

Zou, T.T.[Teng-Tao], Yang, S.M.[Shang-Ming], Zhang, Y.[Yun], Ye, M.[Mao],
Attention guided neural network models for occluded pedestrian detection,
PRL(131), 2020, pp. 91-97.
Elsevier DOI 2004
Pedestrian detection, Occlusion, Convolutional neural networks, Attention networks, Recurrent neural networks BibRef

Qiu, J., Wang, L., Wang, Y., Hu, Y.H.,
Efficient Proposals: Scale Estimation for Object Proposals in Pedestrian Detection Tasks,
SPLetters(27), 2020, pp. 855-859.
IEEE DOI 2006
Proposals, Training, Surveillance, Neural networks, Measurement, Object detection, Cameras, Object detection, multilayer perceptron, video surveillance system BibRef

Huang, E.[Enbo], Su, Z.[Zhuo], Zhou, F.[Fan], Wang, R.[Ruomei],
Learning rebalanced human parsing model from imbalanced datasets,
IVC(99), 2020, pp. 103928.
Elsevier DOI 2006
Human parsing, Semantic segmentation, Imbalanced datasets BibRef

Aggarwal, S.[Surbhi], Babu, R.V.[R. Venkatesh], Chakraborty, A.[Anirban],
Text-based Person Search via Attribute-aided Matching,
WACV20(2606-2614)
IEEE DOI 2006
pedestrian images that best match a given text query. Feature extraction, Semantics, Task analysis, Training, Footwear, Visualization BibRef

Tang, C., Sheng, L., Zhang, Z., Hu, X.,
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization,
ICCV19(4996-5005)
IEEE DOI 2004
image representation, learning (artificial intelligence), pedestrians, video surveillance BibRef

Tyler-Rodrigue, M., Green, R.,
Track Cyclist Detection and Identification using Mask R-CNN and K-means Clustering,
IVCNZ19(1-6)
IEEE DOI 2004
cameras, computerised instrumentation, convolutional neural nets, image colour analysis, image segmentation, image sensors, identification BibRef

Vobecký, A., Uricár, M., Hurych, D., Škoviera, R.,
Advanced Pedestrian Dataset Augmentation for Autonomous Driving,
ADW19(2367-2372)
IEEE DOI 2004
pedestrians, pose estimation, occlusions, autonomous driving applications, people image generation, autonomous driving BibRef

Niu, K., Huang, Y., Wang, L.,
Fusing Two Directions in Cross-Domain Adaption for Real Life Person Search by Language,
WIDER19(1815-1818)
IEEE DOI 2004
image fusion, learning (artificial intelligence), natural language processing, neural nets, video surveillance, Image sentence matching BibRef

Zhang, L., Zhu, X., Chen, X., Yang, X., Lei, Z., Liu, Z.,
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection,
ICCV19(5126-5136)
IEEE DOI 2004
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, feature extraction, image colour analysis, image fusion, infrared imaging, Color BibRef

Liu, W.[Wei], Liao, S.C.[Sheng-Cai], Ren, W.Q.[Wei-Qiang], Hu, W.D.[Wei-Dong], Yu, Y.[Yinan],
High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection,
CVPR19(5182-5191).
IEEE DOI 2002
BibRef

Brazil, G.[Garrick], Liu, X.M.[Xiao-Ming],
Pedestrian Detection With Autoregressive Network Phases,
CVPR19(7224-7233).
IEEE DOI 2002
BibRef

Baeck, P.J., Lewyckyj, N., Beusen, B., Horsten, W., Pauly, K.,
Drone Based Near Real-time Human Detection With Geographic Localization,
Gi4DM19(49-53).
DOI Link 1912
BibRef

Li, T., Wan, W., Huang, Y., Chen, J., Hu, C., Ma, Y.,
Improving Human Parsing by Extracting Global Information Using the Non-Local Operation,
ICIP19(2961-2965)
IEEE DOI 1910
Deep Learning, Human Parsing, Semantic Segmentation, Non-local Operation BibRef

Fang, L.J.[Liang-Ji], Zhao, X.[Xu], Song, X.[Xiao], Zhang, S.Q.[Shi-Quan], Yang, M.[Ming],
Putting the Anchors Efficiently: Geometric Constrained Pedestrian Detection,
ACCV18(V:387-403).
Springer DOI 1906
BibRef

Peng, X., Murphey, Y., Stent, S., Li, Y., Zhao, Z.,
Spatial Focal Loss for Pedestrian Detection in Fisheye Imagery,
WACV19(561-569)
IEEE DOI 1904
cameras, learning (artificial intelligence), object detection, pedestrians, traffic engineering computing, camera system, Task analysis BibRef

Tamura, M., Horiguchi, S., Murakami, T.,
Omnidirectional Pedestrian Detection by Rotation Invariant Training,
WACV19(1989-1998)
IEEE DOI 1904
convolutional neural nets, image processing, object detection, pedestrians, omnidirectional pedestrian detection, Transforms BibRef

Wen, F.[Fang], Lin, Z.H.[Ze-Hang], Yang, Z.G.[Zhen-Guo], Liu, W.Y.[Wen-Yin],
Single-Stage Detector with Semantic Attention for Occluded Pedestrian Detection,
MMMod19(II:414-425).
Springer DOI 1901
BibRef

Zhang, T.R.[Tai-Ran], Lang, C.Y.[Cong-Yan], Xing, J.L.[Jun-Liang],
Realtime Human Segmentation in Video,
MMMod19(II:206-217).
Springer DOI 1901
BibRef

Kataoka, H., Ohki, S., Iwata, K., Satoh, Y.,
Occlusion Handling Human Detection with Refocused Images,
ICPR18(1701-1706)
IEEE DOI 1812
Cameras, Feature extraction, Databases, Training, Arrays, Visualization, Image edge detection BibRef

Zhang, S., Yang, J., Schiele, B.,
Occluded Pedestrian Detection Through Guided Attention in CNNs,
CVPR18(6995-7003)
IEEE DOI 1812
Detectors, Feature extraction, Computer architecture, Object detection, Training, Body regions, Correlation BibRef

Rahman, M.A., Kapoor, P., Laganière, R., Laroche, D., Zhu, C., Xu, X., Osman Ors, A.,
Deep People Detection: A Comparative Study of SSD and LSTM-decoder,
CRV18(305-312)
IEEE DOI 1812
Feature extraction, Computer architecture, Head, Real-time systems, Videos, Detectors, Magnetic heads, deep learning, people detection, SSD BibRef

Noh, J., Lee, S., Kim, B., Kim, G.,
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors,
CVPR18(966-974)
IEEE DOI 1812
Detectors, Object detection, Predictive models, Proposals, Feature extraction, Semantics BibRef

Chavdarova, T., Baqué, P., Bouquet, S., Maksai, A., Jose, C., Bagautdinov, T., Lettry, L., Fua, P., Van Gool, L.J., Fleuret, F.,
WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,
CVPR18(5030-5039)
IEEE DOI 1812
Cameras, Benchmark testing, Computational modeling, Tracking, Synchronization, Detectors BibRef

Song, T.[Tao], Sun, L.[Leiyu], Xie, D.[Di], Sun, H.M.[Hai-Ming], Pu, S.L.[Shi-Liang],
Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation,
ECCV18(VII: 554-569).
Springer DOI 1810
BibRef

Zhou, M.H.[Meng-Han], Ma, J.X.[Jian-Xiang], Ming, A.L.[An-Long], Zhou, Y.[Yu],
Objectness-Aware Tracking via Double-Layer Model,
ICIP18(3713-3717)
IEEE DOI 1809
Proposals, Target tracking, Predictive models, Correlation, Support vector machines, Feature extraction, Objectness Layer BibRef

Chang, Y., Chen, H., Chuang, J., Liao, I.,
Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning,
ICIP18(1917-1921)
IEEE DOI 1809
Machine learning, Feature extraction, Transforms, Object detection, Drones, Image recognition, Computational modeling, vanishing point transformation BibRef

Ulutan, O., Riggan, B.S., Nasrabadi, N.M., Manjunath, B.S.,
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data,
WACV18(1160-1169)
IEEE DOI 1806
cameras, image fusion, image resolution, neural nets, object detection, sensor fusion, spatiotemporal phenomena, vectors, Visualization BibRef

Krams, O., Kiryati, N.,
People detection in top-view fisheye imaging,
AVSS17(1-6)
IEEE DOI 1806
Jacobian matrices, calibration, cameras, feature extraction, image reconstruction, image sensors, lenses, object detection, Standards BibRef

Corbetta, A., Menkovski, V., Toschi, F.,
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields,
AVSS17(1-6)
IEEE DOI 1806
learning (artificial intelligence), neural nets, object detection, object tracking, pedestrians, DL algorithms, Training data BibRef

Ujjwal, U., Dziri, A., Leroy, B., Bremond, F.,
A One-and-Half Stage Pedestrian Detector,
WACV20(765-774)
IEEE DOI 2006
Detectors, Proposals, Feature extraction, Semantics, Training, Complexity theory, Autonomous vehicles BibRef

Trichet, R., Bremond, F.,
LBP Channels for Pedestrian Detection,
WACV18(1066-1074)
IEEE DOI 1806
Haar transforms, feature extraction, image classification, image filtering, image texture, Training BibRef

Weng, X., Wu, S., Beainy, F., Kitani, K.M.,
Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision,
WACV18(1084-1092)
IEEE DOI 1806
cameras, computer vision, convolution, estimation theory, feature extraction, feedforward neural nets, mobile computing, Robustness BibRef

Lim, Y.C.[Young Chul], Kang, M.S.[Min-Sung],
Multi-Pedestrian detection and tracking using unified multi-channel features,
AVSS17(1-5)
IEEE DOI 1806
BibRef
Earlier: A2, A1:
High performance and fast object detection in road environments,
IPTA17(1-6)
IEEE DOI 1804
image classification, image fusion, object detection, object tracking, pedestrians, vectors, data association, Visualization. driver information systems, feedforward neural nets, learning (artificial intelligence), BibRef

Kniaz, V.V., Fedorenko, V.V.,
An Algorithm for Pedestrian Detection in Multispectral Image Sequences,
PTVSBB17(73-77).
DOI Link 1805
BibRef

Dong, P., Wang, W., Fan, M., Wang, R., Li, G.,
Mask-streaming CNN for pedestrian detection,
VCIP17(1-4)
IEEE DOI 1804
feature extraction, image classification, image representation, learning (artificial intelligence), neural nets, Semantic Characters BibRef

Zhang, H., Hu, X., Zhuo, L., Zhang, J.,
Pedestrian Detection Based on Imbalance Prior for Surveillance Video,
DICTA17(1-7)
IEEE DOI 1804
edge detection, feature extraction, image classification, image colour analysis, image resolution, pedestrians, Surveillance BibRef

Williams, J., Carneiro, G., Suter, D.,
Region of Interest Autoencoders with an Application to Pedestrian Detection,
DICTA17(1-8)
IEEE DOI 1804
computer vision, image reconstruction, learning (artificial intelligence), object detection, Training BibRef

García-Martín, A., San Miguel, J.C.,
Adaptive people detection based on cross-correlation maximization,
ICIP17(3385-3389)
IEEE DOI 1803
Correlation, Detectors, Mutual information, Power capacitors, Runtime, Standards, Training, Detector adaptation, Thresholds BibRef

Kruthiventi, S.S.S., Sahay, P., Biswal, R.,
Low-light pedestrian detection from RGB images using multi-modal knowledge distillation,
ICIP17(4207-4211)
IEEE DOI 1803
Data mining, Feature extraction, Knowledge engineering, Lighting, Proposals, Training, Visualization, Convolutional Neural Networks, Low-light pedestrian detection BibRef

Zhang, C., Kim, J.,
Improving channel features using statistical analysis for pedestrian detection,
ICIP17(2329-2333)
IEEE DOI 1803
Feature extraction, Filtering, Head, Histograms, Shape, Statistical analysis, Training, channel features, statistical model BibRef

Yan, Y., Xu, M., Smith, J.S.,
Multiview pedestrian localisation via a prime candidate chart based on occupancy likelihoods,
ICIP17(2334-2338)
IEEE DOI 1803
Benchmark testing, Cameras, Color, Phantoms, Robustness, Transmission line matrix methods, image fusion, visual surveillance BibRef

Ji, Z., Zheng, W., Pang, Y.,
Deep pedestrian attribute recognition based on LSTM,
ICIP17(151-155)
IEEE DOI 1803
Attribute prediction, LSTM, Pedestrian attribute recognition, Visual surveillance. BibRef

Chien, J.T., Chou, C.J., Chen, D.J., Chen, H.T.,
Detecting Nonexistent Pedestrians,
CVRoads17(182-189)
IEEE DOI 1802
Head, Image segmentation, Training, Training data BibRef

Zhou, H.Y., Gao, B.B., Wu, J.,
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors,
ICCV17(3525-3533)
IEEE DOI 1802
image classification, object detection, AF classifier, Adaptive Feeding, Caltech Pedestrian dataset, MS COCO dataset, Real-time systems BibRef

Carletti, V.[Vincenzo], del Pizzo, L.[Luca], Percannella, G.[Gennaro], Vento, M.[Mario],
An efficient and effective method for people detection from top-view depth cameras,
AVSS18(1-6)
IEEE DOI 1806
BibRef
Earlier:
Benchmarking Two Algorithms for People Detection from Top-View Depth Cameras,
CIAP17(I:73-83).
Springer DOI 1711
cameras, computational complexity, computer vision, image sensors, object detection, object tracking, optimisation, Magnetic heads BibRef

Gabriel, E.[Eric], Schramm, H.[Hauke], Meyer, C.[Carsten],
Analysis of the Discriminative Generalized Hough Transform for Pedestrian Detection,
CIAP17(II:104-115).
Springer DOI 1711
BibRef

Verbickas, R., Laganiere, R., Laroche, D., Zhu, C., Xu, X., Ors, A.,
SqueezeMap: Fast Pedestrian Detection on a Low-Power Automotive Processor Using Efficient Convolutional Neural Networks,
ECVW17(463-471)
IEEE DOI 1709
Autonomous vehicles, Cameras, Computational efficiency, Computational modeling, Computer architecture, Fires, Heating, systems BibRef

Eldesokey, A.[Abdelrahman], Felsberg, M.[Michael], Khan, F.S.[Fahad Shahbaz],
Ellipse Detection for Visual Cyclists Analysis 'In the Wild',
CAIP17(I: 319-331).
Springer DOI 1708
BibRef

Chandran, A.K., Subramaniam, A., Wong, W.C., Yang, J., Chaturvedi, K.A.,
A PTZ camera based people-occupancy estimation system (PCBPOES),
MVA17(145-148)
DOI Link 1708
Cameras, Head, Lighting, Magnetic heads, Probabilistic logic, Retina, Support vector machines. BibRef

Wang, H., Gu, Y., Kamijo, S.,
Pedestrian positioning in urban city with the aid of Google maps street view,
MVA17(456-459)
DOI Link 1708
Buildings, Cameras, Google, Image matching, Meters, Urban, areas BibRef

Kuranuki, Y., Patras, I.,
Minimal filtered channel features for pedestrian detection,
ICPR16(681-686)
IEEE DOI 1705
Color, Computer architecture, Decorrelation, Feature extraction, Optical filters, Shape, Training BibRef

Wang, D.[Dan], Zhang, C.Y.[Chong-Yang], Cheng, H.[Hao], Shang, Y.F.[Yan-Feng], Mei, L.[Lin],
SPID: Surveillance Pedestrian Image Dataset and Performance Evaluation for Pedestrian Detection,
BEST16(III: 463-477).
Springer DOI 1704
Dataset, Pedestrians. BibRef

Zhu, Y.S.[You-Song], Wang, J.Q.[Jin-Qiao], Zhao, C.Y.[Chao-Yang], Guo, H.Y.[Hai-Yun], Lu, H.Q.[Han-Qing],
Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection,
ACCV16(II: 416-430).
Springer DOI 1704
BibRef

Cheng, Z.Y.[Zhi-Yi], Li, X.X.[Xiao-Xiao], Loy, C.C.[Chen Change],
Pedestrian Color Naming via Convolutional Neural Network,
ACCV16(II: 35-51).
Springer DOI 1704
BibRef

Alzughaibi, A., Chaczko, Z.,
A precise human detection model using combination of feature extraction techniques in a dynamic environment,
IVCNZ17(1-6)
IEEE DOI 1902
BibRef
Earlier:
Human detection model using feature extraction method in video frames,
ICVNZ16(1-6)
IEEE DOI 1701
feature extraction, image classification, learning (artificial intelligence), object detection, Testing. Computational modeling BibRef

Cao, C., Wang, Y.[Yu], Kato, J.[Jien], Zhang, G.W.[Guan-Wen], Mase, K.J.[Ken-Ji],
Solving Occlusion Problem in Pedestrian Detection by Constructing Discriminative Part Layers,
WACV17(91-99)
IEEE DOI 1609
Data mining, Detectors, Feature extraction, Pipelines, Robustness, Training, Visualization BibRef

Kokubo, Y., Wang, Y.[Yu], Kato, J.[Jien], Zhang, G.W.[Guan-Wen], Mase, K.J.[Ken-Ji],
Add-On Strategies for Fine-Grained Pedestrian Classification,
DICTA16(1-6)
IEEE DOI 1701
Feature extraction BibRef

Bowers, J., Green, R.,
Improving pedestrian detection,
ICVNZ16(1-5)
IEEE DOI 1701
Biological neural networks BibRef

Lee, D.[Donghoon], Cha, G.[Geonho], Yang, M.H.[Ming-Hsuan], Oh, S.H.[Song-Hwai],
Individualness and Determinantal Point Processes for Pedestrian Detection,
ECCV16(VI: 330-346).
Springer DOI 1611
BibRef

Boui, M., Hadj-Abdelkader, H.[Hicham], Ababsa, F.E., Bouyakhf, E.H.,
New approach for human detection in spherical images,
ICIP16(604-608)
IEEE DOI 1610
Adaptation models BibRef

Zhang, S., Zhu, Q., Roy-Chowdhury, A.,
Adaptive algorithm selection, with applications in pedestrian detection,
ICIP16(3768-3772)
IEEE DOI 1610
Algorithm design and analysis BibRef

Correia, A.J.L., Schwartz, W.R.,
Oblique random forest based on partial least squares applied to pedestrian detection,
ICIP16(2931-2935)
IEEE DOI 1610
Computer vision BibRef

Errami, M., Rziza, M.[Mohammed],
Improving Pedestrian Detection Using Support Vector Regression,
CGiV16(156-160)
IEEE DOI 1608
Haar transforms BibRef

Rehder, E., Kloeden, H.,
Goal-Directed Pedestrian Prediction,
CVRoads15(139-147)
IEEE DOI 1602
Context BibRef

Toca, C.[Cosmin], Ciuc, M.[Mihai], Patrascu, C.[Carmen],
Normalized Autobinomial Markov Channels For Pedestrian Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yang, Y.[Yi], Wang, Z.H.[Zhen-Hua], Wu, F.C.[Fu-Chao],
Exploring Prior Knowledge for Pedestrian Detection,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Xu, P.[Philippe], Davoine, F.[Franck], Denoeux, T.[Thierry],
Evidential combination of pedestrian detectors,
BMVC14(xx-yy).
HTML Version. 1410
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Abid, N.[Nesrine], Loukil, K.[Kais], Ayedi, W.[Walid], Ammari, A.C.[Ahmed Chiheb], Abid, M.[Mohamed],
Optimized Parallel Model of Covariance Based Person Detection,
CIAP15(II:287-298).
Springer DOI 1511
BibRef

Arana-Daniel, N.[Nancy], Cibrian-Decena, I.[Isabel],
Recognition of Non-pedestrian Human Forms Through Locally Weighted Descriptors,
CIARP15(751-759).
Springer DOI 1511
BibRef

Xu, R.[Rong], Ueno, S.[Satoshi], Kobayashi, T.[Tatsuya], Makibuchi, N.[Naoya], Naito, S.[Sei],
Human Area Refinement for Human Detection,
CIAP15(II:130-141).
Springer DOI 1511
BibRef

Ma, Z.[Zheng], Yu, L.[Lei], Chan, A.B.[Antoni B.],
Small instance detection by integer programming on object density maps,
CVPR15(3689-3697)
IEEE DOI 1510
BibRef

Jiang, Y.S.[Yun-Sheng], Ma, J.W.[Jin-Wen],
Combination features and models for human detection,
CVPR15(240-248)
IEEE DOI 1510
BibRef

Zhang, S.S.[Shan-Shan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Filtered channel features for pedestrian detection,
CVPR15(1751-1760)
IEEE DOI 1510
BibRef

Hosang, J.[Jan], Omran, M.[Mohamed], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Taking a deeper look at pedestrians,
CVPR15(4073-4082)
IEEE DOI 1510
BibRef

Becker, S.[Stefan], Kieritz, H.[Hilke], Hübner, W.[Wolfgang], Arens, M.[Michael],
On the Benefit of State Separation for Tracking in Image Space with an Interacting Multiple Model Filter,
ICISP16(3-11).
WWW Link. 1606
BibRef

Becker, S.[Stefan], Hubner, W.[Wolfgang], Arens, M.[Michael],
Annotation driven MAP search space estimation for sliding-window based person detection,
MVA15(430-434)
IEEE DOI 1507
Cameras BibRef

Ma, P.[Puhao], Sun, L.[Lei], Ai, H.Z.[Hai-Zhou], Sakai, S.[Shun],
Boosted pedestrian detector adaptation in specific scenes,
MVA15(230-233)
IEEE DOI 1507
Detectors BibRef

Gil, J.I.[Jong-In], Mahmoudpour, S., Kim, M.,
Automatic light control system using fish-eye lens camera,
FCV15(1-3)
IEEE DOI 1506
human detection. object detection BibRef

Blondel, P., Potelle, A., Pégard, C., Lozano, R., Lara, D.,
Dynamic collaboration of far-infrared and visible spectrum for human detection,
ICPR16(698-703)
IEEE DOI 1705
BibRef
Earlier: A1, A2, A3, A4, Only:
Fast and viewpoint robust human detection in uncluttered environments,
VCIP14(522-525)
IEEE DOI 1504
Cameras, Collaboration, Detectors, Feature extraction, Optimization, Stereo image processing, Synchronization. BibRef

Shao, S.[Song], Liu, H.[Hong], Wang, X.D.[Xiang-Dong], Qian, Y.L.[Yue-Liang],
Local Associated Features for Pedestrian Detection,
RoLoD14(513-526).
Springer DOI 1504
BibRef

Hwang, S.[Soonmin], Oh, T.H.[Tae-Hyun], Kweon, I.S.[In So],
A Two Phase Approach for Pedestrian Detection,
IVVT14(459-474).
Springer DOI 1504
BibRef

Wang, X.[Xiao], Chen, J.[Jun], Fang, W.H.[Wen-Hua], Liang, C.[Chao], Zhang, C.J.[Chun-Jie], Hu, R.[Ruimin],
Pedestrian detection from salient regions,
ICIP14(2423-2426)
IEEE DOI 1502
Bayes methods BibRef

Zhang, X.G.[Xing-Guo], Chen, G.[Guoyue], Saruta, K.[Kazuki], Terata, Y.[Yuki],
A Simple Visual Words Selection Strategy for Pedestrian Detection,
ISVC14(I: 658-667).
Springer DOI 1501
BibRef

Tani, Y.[Yuta], Hotta, K.[Kazuhiro],
Robust Human Detection to Pose and Occlusion Using Bag-of-Words,
ICPR14(4376-4381)
IEEE DOI 1412
Accuracy BibRef

de Melo, V.H.C.[Victor Hugo Cunha], Leao, S.[Samir], Menotti, D.[David], Schwartz, W.R.[William Robson],
An Optimized Sliding Window Approach to Pedestrian Detection,
ICPR14(4346-4351)
IEEE DOI 1412
Accuracy BibRef

Nilsson, J.[Jonas], Andersson, P.[Patrik], Gu, I.Y.H.[Irene Y.H.], Fredriksson, J.[Jonas],
Pedestrian Detection Using Augmented Training Data,
ICPR14(4548-4553)
IEEE DOI 1412
Data models BibRef

de Smedt, F.[Floris], Puttemans, S., Goedemé, T.[Toon],
How to reach top accuracy for a visual pedestrian warning system from a car?,
IPTA16(1-6)
IEEE DOI 1703
alarm systems BibRef

de Smedt, F.[Floris], van Beeck, K.[Kristof], Tuytelaars, T.[Tinne], Goedeme, T.[Toon],
The Combinator: Optimal Combination of Multiple Pedestrian Detectors,
ICPR14(3522-3527)
IEEE DOI 1412
Accuracy BibRef

Bartoli, F.[Federico], Lisanti, G.[Giuseppe], Karaman, S.[Svebor], Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto],
Unsupervised Scene Adaptation for Faster Multi-scale Pedestrian Detection,
ICPR14(3534-3539)
IEEE DOI 1412
Accuracy BibRef

Frejlichowski, D.[Dariusz], Gosciewska, K.[Katarzyna], Forczmanski, P.[Pawel], Hofman, R.[Radoslaw],
Human Detection for a Video Surveillance Applied in the 'SmartMonitor' System,
ICCVG14(220-227).
Springer DOI 1410
BibRef

Costea, A.D.[Arthur Daniel], Varga, R., Nedevschi, S.[Sergiu],
Fast Boosting Based Detection Using Scale Invariant Multimodal Multiresolution Filtered Features,
CVPR17(993-1002)
IEEE DOI 1711
Benchmark testing, Feature extraction, Image color analysis, Image resolution, Machine learning, BibRef

Costea, A.D.[Arthur Daniel], Nedevschi, S.[Sergiu],
Semantic Channels for Fast Pedestrian Detection,
CVPR16(2360-2368)
IEEE DOI 1612
BibRef
Earlier:
Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier,
CVPR14(2393-2400)
IEEE DOI 1409
boosting BibRef

Sangineto, E.[Enver],
Statistical and Spatial Consensus Collection for Detector Adaptation,
ECCV14(III: 456-471).
Springer DOI 1408
Adaptation of pedestrian detectors toward specific scenarios. BibRef

Bell, A.E.,
Robust feature vector for efficient human detection,
AIPR13(1-5)
IEEE DOI 1408
data compression BibRef

Sager, H.[Hisham], Hoff, W.[William],
Pedestrian detection in low resolution videos,
WACV14(668-673)
IEEE DOI 1406
Detectors BibRef

Tao, J.L.[Jun-Li], Klette, R.,
Part-Based RDF for Direction Classification of Pedestrians, and a Benchmark,
IVVT14(418-432).
Springer DOI 1504
BibRef
Earlier:
Integrated Pedestrian and Direction Classification Using a Random Decision Forest,
AutoDrive13(230-237)
IEEE DOI 1403
behavioural sciences computing BibRef

Li, Y.R.[Yan-Ran], Yu, S.Q.[Shi-Qi], Wu, S.Y.[Sheng-Yin],
Framelet features for pedestrian detection in noisy depth images,
ICIP13(2949-2952)
IEEE DOI 1402
Pedestrian detection;adaptive selection features;framelet BibRef

Li, Z.X.[Zhi-Xuan], Zhao, Y.Y.[Yan-Yun],
Pedestrian detection in single frame by edgelet-LBP part detectors,
AVSS13(420-425)
IEEE DOI 1311
edge detection BibRef

Barbosa-Anda, F.R., Lerasle, F.[Frédéric], Briand, C., Mekonnen, A.A.[Alhayat Ali],
Soft-Cascade Learning with Explicit Computation Time Considerations,
WACV18(1234-1243)
IEEE DOI 1806
computational complexity, image classification, learning (artificial intelligence), object detection, Tuning BibRef

Mekonnen, A.A.[Alhayat Ali], Lerasle, F.[Frédéric], Herbulot, A.[Ariane], Briand, C.,
People Detection with Heterogeneous Features and Explicit Optimization on Computation Time,
ICPR14(4322-4327)
IEEE DOI 1412
BibRef
Earlier: A1, A2, A3, Only:
Person Detection with a Computation Time Weighted AdaBoost,
ACIVS13(632-644).
Springer DOI 1311
Cascading style sheets BibRef

Rujikietgumjorn, S.[Sitapa], Collins, R.T.[Robert T.],
Optimized Pedestrian Detection for Multiple and Occluded People,
CVPR13(3690-3697)
IEEE DOI 1309
BibRef

Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
How good are detection proposals, really?,
BMVC14(xx-yy).
HTML Version. 1410
Object detectors start with detection proposals. BibRef

Benenson, R.[Rodrigo], Mathias, M.[Markus], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
Seeking the Strongest Rigid Detector,
CVPR13(3666-3673)
IEEE DOI 1309
objects detection; pedestrian detection BibRef

Benenson, R.[Rodrigo], Mathias, M.[Markus], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Fast Stixel Computation for Fast Pedestrian Detection,
CVVT12(III: 11-20).
Springer DOI 1210
BibRef
And:
Pedestrian detection at 100 frames per second,
CVPR12(2903-2910).
IEEE DOI 1208
BibRef

Taiana, M.[Matteo], Nascimento, J.C.[Jacinto C.], Bernardino, A.[Alexandre],
An Improved Labelling for the INRIA Person Data Set for Pedestrian Detection,
IbPRIA13(286-295).
Springer DOI 1307
BibRef

Wang, J.Q.[Jian-Qing], Wang, M.[Min], Qiao, H.[Hong], Keane, J.,
Oriented Gradient Context for pedestrian detection,
ICARCV12(1142-1147).
IEEE DOI 1304
BibRef

Wang, L.[Li], Chan, K.L.[Kap Luk], Wang, G.[Gang],
Human Detection with Occlusion Handling by Over-Segmentation and Clustering on Foreground Regions,
CDF12(II:197-208).
Springer DOI 1304
BibRef

Hao, P.Y.[Peng-Yi], Kamata, S.I.[Sei-Ichiro],
An efficient video retrieval scheme based on facial signatures,
ICIP13(2699-2703)
IEEE DOI 1402
BibRef
Earlier:
Unsupervised people organization and its application on individual retrieval from videos,
ICPR12(2001-2004).
WWW Link. 1302
Linear discriminant analysis;Signature;Video retrieval BibRef

Ahmed, I.[Imran], Carter, J.N.[John N.],
A robust person detector for overhead views,
ICPR12(1483-1486).
WWW Link. 1302
BibRef

Wang, Q.Y.[Qing-Yuan], Pang, J.B.[Jun-Biao], Liu, G.[Guoyi], Qin, L.[Lei], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang],
Color Maximal-Dissimilarity Pattern for pedestrian detection,
ICPR12(1952-1955).
WWW Link. 1302
BibRef

Tasson, D., Montagnini, A., Marzotto, R., Farenzena, M., Cristani, M.,
FPGA-based pedestrian detection under strong distortions,
ECVW15(65-70)
IEEE DOI 1510
Cameras BibRef

Martelli, S.[Samuele], Tosato, D.[Diego], Cristani, M.[Marco], Murino, V.[Vittorio],
Fast FPGA-based architecture for pedestrian detection based on covariance matrices,
ICIP11(389-392).
IEEE DOI 1201
BibRef

Nodari, A.[Angelo], Vanetti, M.[Marco], Gallo, I.[Ignazio],
Digital privacy: Replacing pedestrians from Google Street View images,
ICPR12(2889-2893).
WWW Link. 1302
BibRef

Koyama, T.[Tatsuya], Nakashima, Y.[Yuta], Babaguchi, N.[Noboru],
Markov random field-based real-time detection of intentionally-captured persons,
ICIP12(1377-1380).
IEEE DOI 1302
BibRef

Garcia-Martin, A.[Alvaro], Cavallaro, A.[Andrea], Martinez, J.M.[Jose M.],
People-background segmentation with unequal error cost,
ICIP12(157-160).
IEEE DOI 1302
BibRef

Cao, Y.Y.[Yun-Yun], Pranata, S.[Sugiri], Yasugi, M.[Makoto], Niu, Z.H.[Zhi-Heng], Nishimura, H.[Hirofumi],
Stagged multi-scale LBP for pedestrian detection,
ICIP12(449-452).
IEEE DOI 1302
BibRef

Liu, Z.F.[Zhi-Fang], Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou], Yamashita, T.[Takayoshi],
Adaptation of boosted pedestrian detectors by feature reselection,
ICIP12(481-484).
IEEE DOI 1302
BibRef

Huang, P.J.[Po-Jui], Chen, D.Y.[Duan-Yu],
Robust wheelchair pedestrian detection using sparse representation,
VCIP12(1-5).
IEEE DOI 1302
BibRef

Tang, D.H.[Dan-Hang], Liu, Y.[Yang], Kim, T.K.[Tae-Kyun],
Fast Pedestrian Detection by Cascaded Random Forest with Dominant Orientation Templates,
BMVC12(58).
DOI Link 1301
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Ladický, L.[Lubor], Torr, P.H.S.[Philip H.S.], Zisserman, A.[Andrew],
Latent SVMs for Human Detection with a Locally Affine Deformation Field,
BMVC12(10).
DOI Link 1301
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Evans, M.[Murray], Li, L.Z.[Long-Zhen], Ferryman, J.M.[James M.],
Suppression of Detection Ghosts in Homography Based Pedestrian Detection,
AVSS12(31-36).
IEEE DOI 1211
BibRef

Kamberov, G.[George], Burlick, M.[Matt], Karydas, L.[Lazaros], Koteoglou, O.[Olga],
Scar: Dynamic Adaptation for Person Detection and Persistence Analysis in Unconstrained Videos,
ISVC12(II: 176-187).
Springer DOI 1209
BibRef

Ding, Y.Y.[Yuan-Yuan], Xiao, J.[Jing],
Contextual boost for pedestrian detection,
CVPR12(2895-2902).
IEEE DOI 1208
BibRef

Han, H.[Hong], Fan, Y.J.[You-Jian], Jiao, L.C.[Li-Cheng], Chen, Z.C.[Zhi-Chao],
Concatenated edge and co-occurrence feature extracted from Curvelet Transform for human detection,
IVCNZ10(1-8).
IEEE DOI 1203
BibRef

Munaro, M.[Matteo], Cenedese, A.[Angelo],
Scene specific people detection by simple human interaction,
HICV11(1250-1255).
IEEE DOI 1201
BibRef

Zini, L.[Luca], Odone, F.[Francesca],
Efficient pedestrian detection with group lasso,
VS11(1777-1784).
IEEE DOI 1201
BibRef

Nguyen, D.T.[Duc Thanh],
A Novel Chamfer Template Matching Method Using Variational Mean Field,
CVPR14(2425-2432)
IEEE DOI 1409
Chamfer template matching; object detection; variational mean field BibRef

Nguyen, D.T.[Duc Thanh], Ogunbona, P.[Philip], Li, W.Q.[Wan-Qing],
Detecting humans under occlusion using variational mean field method,
ICIP11(2049-2052).
IEEE DOI 1201
BibRef

Migniot, C.[Cyrille], Bertolino, P.[Pascal], Chassery, J.M.[Jean-Marc],
Automatic people segmentation with a template-driven graph cut,
ICIP11(3149-3152).
IEEE DOI 1201
BibRef

Wu, J.C.[Jin-Chen], Chen, W.[Wei], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Partial Least Squares based subwindow search for pedestrian detection,
ICIP11(3565-3568).
IEEE DOI 1201
BibRef

Cao, Y.Y.[Yun-Yun], Pranata, S.[Sugiri], Nishimura, H.[Hirofumi],
Local Binary Pattern features for pedestrian detection at night/dark environment,
ICIP11(2053-2056).
IEEE DOI 1201
BibRef

Chen, X.T.[Xiao-Tang], Huang, K.Q.[Kai-Qi], Tan, T.N.[Tie-Niu],
Direction-based stochastic matching for pedestrian recognition in non-overlapping cameras,
ICIP11(2065-2068).
IEEE DOI 1201
BibRef

Wang, J.Q.[Jun-Qiang], Ma, H.D.[Hua-Dong],
MPL-Boosted Integrable Features Pool for pedestrian detection,
ICIP11(805-808).
IEEE DOI 1201
BibRef

Ma, Y.D.[Ying-Dong], Chen, X.[Xiankai], Jin, L.[Liu], Chen, G.[George],
A Monocular Human Detection System Based on EOH and Oriented LBP Features,
ISVC11(I: 551-562).
Springer DOI 1109
BibRef

El Guebaly, T.[Tarek], Bouguila, N.[Nizar],
A nonparametric Bayesian approach for enhanced pedestrian detection and foreground segmentation,
OTCBVS11(21-26).
IEEE DOI 1106
BibRef

Kim, D.H.[Dae-Hwan], Kim, Y.[Yeonho], Kim, D.J.[Dai-Jin],
Separating Occluded Humans by Bayesian Pixel Classifier with Re-weighted Posterior Probability,
ACIVS11(543-553).
Springer DOI 1108
BibRef

Pedrocca, P.J.[Pablo Julian], Allili, M.S.[Mohand Saïd],
Real-Time People Detection in Videos Using Geometrical Features and Adaptive Boosting,
ICIAR11(I: 314-324).
Springer DOI 1106
BibRef

Zheng, Y.B.[Yong-Bin], Shen, C.H.[Chun-Hua], Hartley, R.I.[Richard I.], Huang, X.S.[Xin-Sheng],
Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection,
ACCV10(IV: 281-292).
Springer DOI 1011
BibRef

Leithy, A.[Alaa], Moustafa, M.N.[Mohamed N.], Wahba, A.[Ayman],
Fast and Accurate Pedestrian Detection Using a Cascade of Multiple Features,
VS10(153-163).
Springer DOI 1109
BibRef
And:
Cascade of Complementary Features for Fast and Accurate Pedestrian Detection,
PSIVT10(343-348).
IEEE DOI 1011
BibRef

Barnich, O.[Olivier], Piérard, S.[Sébastien], van Droogenbroeck, M.[Marc],
A Virtual Curtain for the Detection of Humans and Access Control,
ACIVS10(II: 98-109).
Springer DOI 1012
BibRef

Yu, J.[Jie], Farin, D.[Dirk], Kruger, C.[Christof], Schiele, B.[Bernt],
Improving person detection using synthetic training data,
ICIP10(3477-3480).
IEEE DOI 1009
BibRef

Middleton, L.[Lee], Snowdon, J.R.[James R.],
Histogram of confidences for person detection,
ICIP10(1841-1844).
IEEE DOI 1009
BibRef

Garcia-Martin, A., Martinez, J.M.,
Robust Real Time Moving People Detection in Surveillance Scenarios,
AVSS10(241-247).
IEEE DOI 1009
BibRef

Shen, J.L.[Jia-Li], Yan, W.Q.[Wei-Qi], Miller, P., Zhou, H.Y.[Hui-Yu],
Human Localization in a Cluttered Space Using Multiple Cameras,
AVSS10(85-90).
IEEE DOI 1009
BibRef

Atienza-Vanacloig, V.[Vicente], Rosell-Ortega, J.[Juan], Andreu-Garcia, G.[Gabriela], Valiente-Gonalez, J.M.[Jose Miguel],
Locating People in Images by Optimal Cue Integration,
ICPR10(1804-1807).
IEEE DOI 1008
BibRef

Heimonen, T.A.[Teuvo Antero], Heikkila, J.[Janne],
A Human Detection Framework for Heavy Machinery,
ICPR10(416-419).
IEEE DOI 1008
BibRef

Ma, W.H.[Wen-Hua], He, P.[Peng], Huang, L.[Lei], Liu, C.P.[Chang-Ping],
Context Inspired Pedestrian Detection in Far-Field Videos,
ICPR10(3009-3012).
IEEE DOI 1008
BibRef

Hong, X.P.[Xiao-Peng], Chang, H.[Hong], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Boosted Sigma Set for Pedestrian Detection,
ICPR10(3017-3020).
IEEE DOI 1008
See also Sigma Set: A small second order statistical region descriptor. BibRef

Cai, Y.H.[Ying-Hao], Takala, V.[Valtteri], Pietikainen, M.[Matti],
Matching Groups of People by Covariance Descriptor,
ICPR10(2744-2747).
IEEE DOI 1008
BibRef

Simonnet, D.[Damien], Velastin, S.A.[Sergio A.],
Pedestrian detection based on adaboost algorithm with a pseudo-calibrated camera,
IPTA10(54-59).
IEEE DOI 1007
BibRef

Flores, A.[Arturo], Belongie, S.J.[Serge J.],
Removing pedestrians from Google street view images,
IWMV10(53-58).
IEEE DOI 1006
BibRef

Ott, P.[Patrick], Everingham, M.[Mark],
Implicit color segmentation features for pedestrian and object detection,
ICCV09(723-730).
IEEE DOI 0909
BibRef

Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang], Wu, Z.P.[Zhi-Peng],
Transfer pedestrian detector towards view-adaptiveness and efficiency,
ObjectEvent09(609-616).
IEEE DOI 0910
BibRef

Hou, Y.L.[Ya-Li], Pang, G.K.H.[Grantham K.H.],
Human detection in crowded scenes,
ICIP10(721-724).
IEEE DOI 1009
BibRef
Earlier:
Human detection in a challenging situation,
ICIP09(2561-2564).
IEEE DOI 0911
BibRef

Liao, C.T.[Chia-Te], Lai, S.H.[Shang-Hong], Wang, W.H.[Wen-Hao],
A hierarchical image kernel with application to pedestrian identification for video surveillance,
ICIP09(1125-1128).
IEEE DOI 0911
BibRef

Yu, X.G.[Xin-Guo], Dong, L.[Li], Li, L.Y.[Li-Yuan], Hoe, J.K.E.[Jerry Kah Eng],
Lift-button detection and recognition for service robot in buildings,
ICIP09(313-316).
IEEE DOI 0911
BibRef

Li, L.Y.[Li-Yuan], Hoe, J.K.E.[Jerry Kah Eng], Yan, S.C.[Shui-Cheng], Yu, X.G.[Xin-Guo],
ML-fusion based multi-model human detection and tracking for robust human-robot interfaces,
WACV09(1-8).
IEEE DOI 0912
BibRef

Bolme, D.S.[David S.], Beveridge, J.R.[J. Ross], Draper, B.A.[Bruce A.], Lui, Y.M.[Yui Man],
Visual object tracking using adaptive correlation filters,
CVPR10(2544-2550).
IEEE DOI 1006
BibRef
Earlier: A1, A4, A3, A2:
Simple real-time human detection using a single correlation filter,
PETS-Winter09(1-8).
IEEE DOI 0912
BibRef

Lai, J., Ford, J.J., O'Shea, P., Walker, R.,
Hidden Markov Model Filter Banks for Dim Target Detection from Image Sequences,
DICTA08(312-319).
IEEE DOI 0812
BibRef

Yu, L.P.[Li-Ping], Yao, W.[Wentao],
Pedestrian Detection Fusion Method Based on Mean Shift,
ICMV09(204-207).
IEEE DOI 0912
BibRef

Rapus, M.[Martin], Munder, S.[Stefan], Baratoff, G.[Gregory], Denzler, J.[Joachim],
Pedestrian Detection by Probabilistic Component Assembly,
DAGM09(91-100).
Springer DOI 0909
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Pang, J.B.[Jun-Biao], Huang, Q.M.[Qing-Ming], Jiang, S.Q.[Shu-Qiang],
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Jones, M.J.[Michael J.], Snow, D.[Daniel],
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Park, J.M.[Jung-Me], Luo, Y.[Yun], Wang, H.X.[Hao-Xing], Murphey, Y.L.[Yi L.],
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Abramson, Y., Steux, B.,
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Colombo, A.[Alberto], Orwell, J.[James], Velastin, S.A.[Sergio A.],
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Real-Time Humans Detection in Urban Scenes,
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Duan, G.Q.[Gen-Quan], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
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Springer DOI 1011
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And:
A Structural Filter Approach to Human Detection,
ECCV10(VI: 238-251).
Springer DOI 1009
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Duan, G.Q.[Gen-Quan], Huang, C.[Chang], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
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Gao, W.[Wei], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Adaptive Contour Features in oriented granular space for human detection and segmentation,
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Hou, C.[Cong], Ai, H.Z.[Hai-Zhou], Lao, S.H.[Shi-Hong],
Multiview Pedestrian Detection Based on Vector Boosting,
ACCV07(I: 210-219).
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Chen, Y.T.[Yu-Ting], Chen, C.S.[Chu-Song],
A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection,
ACCV07(I: 905-914).
Springer DOI 0711
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Schulz, W.[Wolfgang], Enzweiler, M.[Markus], Ehlgen, T.[Tobias],
Pedestrian Recognition from a Moving Catadioptric Camera,
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Shet, V.D.[Vinay D.], Neumann, J.[Jan], Ramesh, V.[Visvanathan], Davis, L.S.[Larry S.],
Bilattice-based Logical Reasoning for Human Detection,
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Gallagher, A.C.[Andrew C.], Blose, A.C.[Andrew C.], Chen, T.H.[Tsu-Han],
Jointly Estimating Demographics and Height with a Calibrated Camera,
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Gallagher, A.C.[Andrew C.], Chen, T.H.[Tsu-Han],
Using a Markov Network to Recognize People in Consumer Images,
ICIP07(IV: 489-492).
IEEE DOI 0709
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Using Group Prior to Identify People in Consumer Images,
SLAM07(1-8).
IEEE DOI 0706
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Feris, R.S.[Rogerio S.], Tian, Y.L.[Ying-Li], Hampapur, A.[Arun],
Capturing People in Surveillance Video,
VS07(1-8).
IEEE DOI 0706
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Parikh, D.[Devi], Zitnick, C.L.[C. Lawrence],
Finding the weakest link in person detectors,
CVPR11(1425-1432).
IEEE DOI 1106
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Sivic, J., Zitnick, C.L., Szeliski, R.S.[Richard S.],
Finding people in repeated shots of the same scene,
BMVC06(III:909).
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Davis, L.S.[Larry S.],
Segmenting people in small groups,
VSSN06(1-2).
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Harasse, S.[Sebastien], Bonnaud, L.[Laurent], Desvignes, M.[Michel],
A Human Model for Detecting People in Video from Low Level Features,
ICIP06(1845-1848).
IEEE DOI 0610
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Earlier:
Human model for people detection in dynamic scenes,
ICPR06(I: 335-354).
IEEE DOI 0609
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Scotti, G., Cuocolo, A., Coelho, C., Marchesotti, L.,
A Novel Pedestrian Classification Algorithm for a High Definition Dual Camera 360 Degrees Surveillance System,
ICIP05(III: 880-883).
IEEE DOI 0512
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Mori, G.[Greg],
Guiding Model Search Using Segmentation,
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IEEE DOI 0510
Segmentation into "suprepixels" (small regions). Human model composed of sets of superpixels, joints in center of one. BibRef

Zhao, L.[Liang], Davis, L.S.[Larry S.],
Closely Coupled Object Detection and Segmentation,
ICCV05(I: 454-461).
IEEE DOI 0510
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And:
Segmentation and Appearance Model Building from an Image Sequence,
ICIP05(I: 321-324).
IEEE DOI 0512
Link detection and segmentation, not separate tasks. BibRef

Castillo, C.[Carlos], Chang, C.[Carolina],
An Approach to Vision-Based Person Detection in Robotic Applications,
IbPRIA05(I:209).
Springer DOI 0509
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Liu, Z.Y.[Zong-Yi], Sarkar, S.[Sudeep],
Challenges in Segmentation of Human Forms in Outdoor Video,
PercOrg04(43).
IEEE DOI 0502
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Owechko, Y., Medasani, S.,
A Swarm-Based Volition/Attention Framework for Object Recognition,
AttenPerf05(III: 91-91).
IEEE DOI 0507
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Lombardi, P., Zavidovique, B.,
A context-dependent vision system for pedestrian detection,
IVS04(578-583).
IEEE DOI 0411
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And:
Architectural design issues for bayesian contextual vision,
ICPR04(I: 753-756).
IEEE DOI 0409
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Dante, A., Brookes, M., Constantinides, A.G.,
Robust multi-body segmentation,
BMVC03(xx-yy).
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Ramoser, H., Schlogl, T., Beleznai, C., Winter, M., Bischof, H.,
Shape-based detection of humans for video surveillance applications,
ICIP03(III: 1013-1016).
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Lefee, D., Mousset, S., Bertozzi, M., Bensrhair, A.,
Cooperation of passive vision systems in detection and tracking of pedestrians,
IVS04(768-773).
IEEE DOI 0411
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Sprague, N., Luo, J.B.[Jie-Bo],
Clothed people detection in still images,
ICPR02(III: 585-589).
IEEE DOI 0211
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Vendrig, J.[Jeroen], Worring, M.[Marcel],
Multimodal Person Identification in Movies,
CIVR02(175-185).
Springer DOI 0208
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Utsumi, A., Tetsutani, N.,
Human detection using geometrical pixel value structures,
AFGR02(34-39).
IEEE DOI 0206
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Pujol, A., Lumbreras, F., Varona, X., Villanueva, J.J.[Juan J.],
Locating People in Indoor Scenes for Real Applications,
ICPR00(Vol IV: 632-635).
IEEE DOI 0009
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Lee, M.S.[Mi-Suen],
Detecting People in Cluttered Indoor Scenes,
CVPR00(I: 804-809).
IEEE DOI 0005
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Faulhaber, D., Niemann, H., Weierich, P.,
Detection of Crowds of People by Use of Wavelet Features and Parameter Free Statistical Models,
MVA98(xx-yy). BibRef 9800

Steffens, J.B.[Johannes Bernhard], Elagin, E.V.[Egor Valerievich], Neven, H.[Hartmut],
PersonSpotter: Fast and Robust System for Human Detection, Tracking and Recognition,
AFGR98(516-521).
IEEE DOI BibRef 9800

Kuno, Y., Watanabe, T., Shimosakoda, Y., Nakagawa, S.,
Automated Detection of Human for Visual Surveillance System,
ICPR96(III: 865-869).
IEEE DOI 9608
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Kosugi, M.[Makoto], Yamashita, K.[Kouji],
Person identification system based on a trapezoid pyramid architecture of a gray-level image,
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Learning, Neural Nets for Human Detection, People Detection, Pedestrians .

Last update:Jun 29, 2020 at 10:24:28