17.1.3.2.2 Local Features, LBP, Patterns, for Pedestrian Detection, People Detection

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

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
Correlation, Covariance matrices, Feature extraction, Manifolds, Standards, Brownian descriptor, covariance descriptor, pedestrian detection, reidentification 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

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

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

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

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), 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

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

Wang, M.J.[Min-Jun], Chen, H.J.[Hou-Jin], Li, Y.F.[Yan-Feng], You, Y.H.[Yu-Hao], Zhu, J.L.[Jin-Lei],
Multi-scale pedestrian detection based on self-attention and adaptively spatial feature fusion,
IET-ITS(15), No. 6, 2021, pp. 837-849.
DOI Link 2106
BibRef

Tan, Y.Z.[Yu-Zhi], Yao, H.X.[Hong-Xun], Li, H.R.[Hao-Ran], Lu, X.S.[Xiu-Sheng], Xie, H.Z.[Hao-Zhe],
PRF-Ped: Multi-scale Pedestrian Detector with Prior-based Receptive Field,
ICPR21(6059-6064)
IEEE DOI 2105
Location awareness, Semantics, Detectors, Feature extraction BibRef

Li, G.[Gang], Zhang, S.S.[Shan-Shan], Yang, J.[Jian],
Nighttime Pedestrian Detection Based on Feature Attention and Transformation,
ICPR21(9180-9187)
IEEE DOI 2105
Lighting, Detectors, Feature extraction, Video surveillance, Fats, Noise measurement BibRef

Tezcan, M.O.[M. Ozan], Duan, Z.H.[Zhi-Hao], Cokbas, M.[Mertcan], Ishwar, P.[Prakash], Konrad, J.[Janusz],
WEPDTOF: A Dataset and Benchmark Algorithms for In-the-Wild People Detection and Tracking from Overhead Fisheye Cameras,
WACV22(1381-1390)
IEEE DOI 2202
Surveillance, Coherence, Benchmark testing, Cameras, Optics, Spatiotemporal phenomena, Datasets, Object Detection/Recognition/Categorization BibRef

Duan, Z.H.[Zhi-Hao], Tezcan, M.O.[M. Ozan], Nakamura, H., Ishwar, P.[Prakash], Konrad, J.[Janusz],
RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images,
OmniCV20(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

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., 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, 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

Song, T.[Tao], Sun, L.Y.[Lei-Yu], 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

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

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

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

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

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

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

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

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

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

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

Park, W.J.[Won-Jae], Kim, D.H.[Dae-Hwan], Suryanto, Lyuh, C.G.[Chun-Gi], Roh, T.M.[Tae Moon], Ko, S.J.[Sung-Jea],
Fast human detection using selective block-based HOG-LBP,
ICIP12(601-604).
IEEE DOI 1302
BibRef

Wang, Q.Y.[Qing-Yuan], Pang, J.B.[Jun-Biao], Liu, G.Y.[Guo-Yi], 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

Zhang, Y.[Ying], Li, S.T.[Shu-Tao],
Gabor-LBP Based Region Covariance Descriptor for Person Re-identification,
ICIG11(368-371).
IEEE DOI 1109
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

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

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

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

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

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

Meng, L.[Long], Li, L.[Liang], Mei, S.Q.[Shu-Qi], 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

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

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

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
HoG, Gradients, Histogram of Gradients for Human Detection, People Detection, Pedestrians .

Last update:Nov 26, 2024 at 16:40:19