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Earlier:
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Earlier:
Fast and Scalable Enrollment for Face Identification Based on Partial
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FG13(1-8)
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Face identification.
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CIARP11(181-188).
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Earlier: A1, A3, A2:
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Storing moving object information for later surveillance analysis.
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feature extraction
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An Improvement of Energy-Transfer Features Using DCT for Face Detection,
ICISP14(511-519).
Springer DOI
1406
BibRef
Earlier:
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AVSS13(147-152)
IEEE DOI
1311
Haar transforms
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ISVC13(II:425-434).
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1311
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Haque, S.M.,
Babu, R.V.[R. Venkatesh],
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Sparse Representation-Based Human Detection:
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SIViP(10), No. 3, March 2016, pp. 585-592.
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1602
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Pedestrian detection
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Decision trees
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IET-ITS(10), No. 6, 2016, pp. 438-444.
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1608
computer vision
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Efficient Labelling of Pedestrian Supervisions,
ELCVIA(15), No. 1, 2016, pp. 77-99.
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Pedestrian detection
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Computational modeling
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Hwang, J.N.,
Meng, D.,
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Cameras
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Weakly supervised learning
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ITS(18), No. 2, February 2017, pp. 269-281.
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Bayes methods
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Additives
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Proposals, Feature extraction, Support vector machines,
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Computational efficiency, Decision trees, Detectors,
Feature extraction, Intelligent transportation systems,
Quantization (signal), Training, Local co-occurrence,
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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
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
Pfeifer, L.[Lienhard],
Shearlet Features for Pedestrian Detection,
JMIV(61), No. 3, March 2019, pp. 292-309.
WWW Link.
1903
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
Zhang, T.L.[Tian-Liang],
Han, Z.J.[Zhen-Jun],
Xu, H.J.[Hui-Juan],
Zhang, B.C.[Bao-Chang],
Ye, Q.X.[Qi-Xiang],
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian
Detection,
ITS(21), No. 11, November 2020, pp. 4593-4604.
IEEE DOI
2011
Feature extraction, Object detection, Training, Visualization,
Proposals, Cameras, Detectors, CircleNet, feature learning,
traffic scenes
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
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
Zhao, J.[Jian],
Li, J.S.[Jian-Shu],
Liu, H.Z.[Heng-Zhu],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Fine-Grained Multi-human Parsing,
IJCV(128), No. 8-9, September 2020, pp. 2185-2203.
Springer DOI
2008
BibRef
Chiang, S.H.[Sheng-Ho],
Wang, T.[Tsaipei],
Chen, Y.F.[Yi-Fu],
Efficient pedestrian detection in top-view fisheye images using
compositions of perspective view patches,
IVC(105), 2021, pp. 104069.
Elsevier DOI
2101
Pedestrian detection, Fisheye cameras, Omnidirectional cameras
BibRef
Yang, X.Y.[Xing-Yi],
Wang, Y.[Yong],
Laganičre, R.[Robert],
A Scale-aware Yolo Model for Pedestrian Detection,
ISVC20(II:15-26).
Springer DOI
2103
BibRef
Ghasemi, M.,
Varshosaz, M.,
Pirasteh, S.,
Evaluating Sector Ring Histogram of Oriented Gradients Filter In
Locating Humans Within UAV Images,
ISPRS20(B2:23-27).
DOI Link
2012
BibRef
Zhou, K.L.[Kai-Lai],
Chen, L.S.[Lin-Sen],
Cao, X.[Xun],
Improving Multispectral Pedestrian Detection by Addressing Modality
Imbalance Problems,
ECCV20(XVIII:787-803).
Springer DOI
2012
BibRef
Yang, L.[Lu],
Song, Q.[Qing],
Wang, Z.H.[Zhi-Hui],
Hu, M.J.[Meng-Jie],
Liu, C.[Chun],
Xin, X.S.[Xue-Shi],
Jia, W.H.[Wen-He],
Xu, S.C.[Song-Cen],
Renovating Parsing R-CNN for Accurate Multiple Human Parsing,
ECCV20(XII: 421-437).
Springer DOI
2010
BibRef
Duan, Z.,
Tezcan, M.O.[M. Ozan],
Nakamura, H.,
Ishwar, P.,
Konrad, J.,
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images,
CmniCV20(2700-2709)
IEEE DOI
2008
Feature extraction, Cameras, Head, Standards, Prediction algorithms,
Object detection, Detectors
BibRef
Wu, J.,
Zhou, C.,
Yang, M.,
Zhang, Q.,
Li, Y.,
Yuan, J.,
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians,
CVPR20(13427-13436)
IEEE DOI
2008
Proposals, Electron tubes, Detectors, Feature extraction,
Reliability, Videos, Semantics
BibRef
Simon, J.[Jules],
Bilodeau, G.A.[Guillaume-Alexandre],
Steele, D.[David],
Mahadik, H.[Harshad],
Color Inference from Semantic Labeling for Person Search in Videos,
ICIAR20(I:139-151).
Springer DOI
2007
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
Vobecký, A.,
Uricár, M.,
Hurych, D.,
koviera, 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
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
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
Rahman, M.A.,
Kapoor, P.,
Laganičre, 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
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
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
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
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
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
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
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
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],
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Evidential combination of pedestrian detectors,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
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
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, 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
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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
BibRef
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
BibRef
Evans, M.[Murray],
Li, L.Z.[Long-Zhen],
Ferryman, J.M.[James M.],
Suppression of Detection Ghosts in Homography Based Pedestrian
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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 Saďd],
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
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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
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
BibRef
Pang, J.B.[Jun-Biao],
Huang, Q.M.[Qing-Ming],
Jiang, S.Q.[Shu-Qiang],
Multiple Instance Boost Using Graph Embedding Based Decision Stump for
Pedestrian Detection,
ECCV08(IV: 541-552).
Springer DOI
0810
BibRef
Lu, H.C.[Hu-Chuan],
Jia, C.H.[Chun-Hua],
Zhang, R.J.[Rui-Juan],
An effective method for detection and segmentation of the body of human
in the view of a single stationary camera,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Thome, N.[Nicolas],
Ambellouis, S.[Sebastien],
A bottom-up, view-point invariant human detector,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Meng, L.[Long],
Li, L.[Liang],
Mei, S.[Shuqi],
Wu, W.G.[Wei-Guo],
Directional entropy feature for human detection,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Jones, M.J.[Michael J.],
Snow, D.[Daniel],
Pedestrian detection using boosted features over many frames,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Park, J.M.[Jung-Me],
Luo, Y.[Yun],
Wang, H.X.[Hao-Xing],
Murphey, Y.L.[Yi L.],
Pedestrian detection by modeling local convex shape features,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Leyrit, L.[Laetitia],
Chateau, T.[Thierry],
Tournayre, C.[Christophe],
Lapreste, J.T.[Jean-Thierry],
Visual pedestrian recognition in weak classifier space using nonlinear
parametric models,
ICIP08(2392-2395).
IEEE DOI
0810
BibRef
Abramson, Y.,
Steux, B.,
Hardware-friendly pedestrian detection and impact prediction,
IVS04(590-595).
IEEE DOI
0411
BibRef
Colombo, A.[Alberto],
Orwell, J.[James],
Velastin, S.A.[Sergio A.],
Colour Constancy Techniques for Re-Recognition of Pedestrians from
Multiple Surveillance Cameras,
M2SFA208(xx-yy).
0810
BibRef
Zhang, C.[Cha],
Hamid, R.[Raffay],
Zhang, Z.Y.[Zheng-You],
Taylor expansion based classifier adaptation:
Application to person detection,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Begard, J.,
Allezard, N.,
Sayd, P.,
Real-time human detection in urban scenes: Local descriptors and
classifiers selection with AdaBoost-like algorithms,
OTCBVS08(1-8).
IEEE DOI
0806
BibRef
Earlier:
Real-Time Humans Detection in Urban Scenes,
BMVC07(xx-yy).
PDF File.
0709
BibRef
Duan, G.Q.[Gen-Quan],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Human Detection in Video over Large Viewpoint Changes,
ACCV10(II: 683-696).
Springer DOI
1011
BibRef
And:
A Structural Filter Approach to Human Detection,
ECCV10(VI: 238-251).
Springer DOI
1009
BibRef
Duan, G.Q.[Gen-Quan],
Huang, C.[Chang],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Boosting Associated Pairing Comparison Features for pedestrian
detection,
VS09(1097-1104).
IEEE DOI
0910
See also High-Performance Rotation Invariant Multiview Face Detection.
BibRef
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,
CVPR09(1786-1793).
IEEE DOI
0906
BibRef
Hou, C.[Cong],
Ai, H.Z.[Hai-Zhou],
Lao, S.H.[Shi-Hong],
Multiview Pedestrian Detection Based on Vector Boosting,
ACCV07(I: 210-219).
Springer DOI
0711
See also High-Performance Rotation Invariant Multiview Face Detection.
BibRef
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
BibRef
Schulz, W.[Wolfgang],
Enzweiler, M.[Markus],
Ehlgen, T.[Tobias],
Pedestrian Recognition from a Moving Catadioptric Camera,
DAGM07(456-465).
Springer DOI
0709
BibRef
Shet, V.D.[Vinay D.],
Neumann, J.[Jan],
Ramesh, V.[Visvanathan],
Davis, L.S.[Larry S.],
Bilattice-based Logical Reasoning for Human Detection,
CVPR07(1-8).
IEEE DOI
0706
BibRef
Gallagher, A.C.[Andrew C.],
Blose, A.C.[Andrew C.],
Chen, T.H.[Tsu-Han],
Jointly Estimating Demographics and Height with a Calibrated Camera,
ICCV09(1187-1194).
IEEE DOI
0909
See also ground truth based vanishing point detection algorithm, A.
BibRef
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
BibRef
And:
Using Group Prior to Identify People in Consumer Images,
SLAM07(1-8).
IEEE DOI
0706
BibRef
Feris, R.S.[Rogerio S.],
Tian, Y.L.[Ying-Li],
Hampapur, A.[Arun],
Capturing People in Surveillance Video,
VS07(1-8).
IEEE DOI
0706
BibRef
Parikh, D.[Devi],
Zitnick, C.L.[C. Lawrence],
Finding the weakest link in person detectors,
CVPR11(1425-1432).
IEEE DOI
1106
BibRef
Sivic, J.,
Zitnick, C.L.,
Szeliski, R.S.[Richard S.],
Finding people in repeated shots of the same scene,
BMVC06(III:909).
PDF File.
0609
BibRef
Davis, L.S.[Larry S.],
Segmenting people in small groups,
VSSN06(1-2).
WWW Link.
0701
BibRef
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
BibRef
Earlier:
Human model for people detection in dynamic scenes,
ICPR06(I: 335-354).
IEEE DOI
0609
BibRef
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
BibRef
Mori, G.[Greg],
Guiding Model Search Using Segmentation,
ICCV05(II: 1417-1423).
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
BibRef
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
BibRef
Liu, Z.Y.[Zong-Yi],
Sarkar, S.[Sudeep],
Challenges in Segmentation of Human Forms in Outdoor Video,
PercOrg04(43).
IEEE DOI
0502
BibRef
Owechko, Y.,
Medasani, S.,
A Swarm-Based Volition/Attention Framework for Object Recognition,
AttenPerf05(III: 91-91).
IEEE DOI
0507
BibRef
Lombardi, P.,
Zavidovique, B.,
A context-dependent vision system for pedestrian detection,
IVS04(578-583).
IEEE DOI
0411
BibRef
And:
Architectural design issues for bayesian contextual vision,
ICPR04(I: 753-756).
IEEE DOI
0409
BibRef
Dante, A.,
Brookes, M.,
Constantinides, A.G.,
Robust multi-body segmentation,
BMVC03(xx-yy).
HTML Version.
0409
BibRef
Ramoser, H.,
Schlogl, T.,
Beleznai, C.,
Winter, M.,
Bischof, H.,
Shape-based detection of humans for video surveillance applications,
ICIP03(III: 1013-1016).
IEEE DOI
0312
BibRef
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
See also Vehicle Detection by Means of Stereo Vision-Based Obstacles Features Extraction and Monocular Pattern Analysis.
BibRef
Sprague, N.,
Luo, J.B.[Jie-Bo],
Clothed people detection in still images,
ICPR02(III: 585-589).
IEEE DOI
0211
BibRef
Vendrig, J.[Jeroen],
Worring, M.[Marcel],
Multimodal Person Identification in Movies,
CIVR02(175-185).
Springer DOI
0208
BibRef
Utsumi, A.,
Tetsutani, N.,
Human detection using geometrical pixel value structures,
AFGR02(34-39).
IEEE DOI
0206
BibRef
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
BibRef
Lee, M.S.[Mi-Suen],
Detecting People in Cluttered Indoor Scenes,
CVPR00(I: 804-809).
IEEE DOI
0005
BibRef
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
(Kanasi Laboratory, J)
BibRef
Kosugi, M.[Makoto],
Yamashita, K.[Kouji],
Person identification system based on a trapezoid pyramid architecture
of a gray-level image,
CIAP97(II: 501-508).
Springer DOI
9709
BibRef
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 .