Viola, P.[Paul],
Jones, M.J.[Michael J.],
Snow, D.[Daniel],
Detecting Pedestrians Using Patterns of Motion and Appearance,
IJCV(63), No. 2, July 2005, pp. 153-161.
Springer DOI
0501
BibRef
Earlier:
ICCV03(734-741).
IEEE DOI
0311
Award, Marr Prize.
BibRef
Foresti, G.L.,
Micheloni, C.,
Piciarelli, C.,
Detecting moving people in video streams,
PRL(26), No. 14, 15 October 2005, pp. 2232-2243.
Elsevier DOI
0510
See also Trajectory-Based Anomalous Event Detection.
BibRef
Liu, Y.Z.[Ya-Zhou],
Chen, X.L.[Xi-Lin],
Yao, H.X.[Hong-Xun],
Cui, X.Y.[Xin-Yi],
Liu, C.R.[Chao-Ran],
Gao, W.[Wen],
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A space-time approach for robust pedestrian detection,
PRL(30), No. 2, 15 January 2009, pp. 148-156,.
Elsevier DOI
0804
Contour-motion feature; Space-time analysis; Pedestrian detection;
Human activity analysis; Event analysis; 3D Haar filter
BibRef
Liu, Y.Z.[Ya-Zhou],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Heikkila, J.[Janne],
Gao, W.[Wen],
Pietikainen, M.[Matti],
Spatial-Temporal Granularity-Tunable Gradients Partition (STGGP)
Descriptors for Human Detection,
ECCV10(I: 327-340).
Springer DOI
1009
BibRef
Liu, Y.Z.[Ya-Zhou],
Heikkila, J.[Janne],
Isotropic Granularity-tunable gradients partition (IGGP) descriptors
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BMVC10(xx-yy).
HTML Version.
1009
BibRef
Liu, Y.Z.[Ya-Zhou],
Shan, S.G.[Shi-Guang],
Zhang, W.C.[Wen-Chao],
Chen, X.L.[Xi-Lin],
Gao, W.[Wen],
Granularity-tunable gradients partition (GGP) descriptors for human
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CVPR09(1255-1262).
IEEE DOI
0906
Hough space descriptors. Spatial and statistical properties.
See also Fea-Accu cascade for face detection.
BibRef
Pang, J.B.[Jun-Biao],
Huang, Q.M.[Qing-Ming],
Jiang, S.Q.[Shu-Qiang],
Gao, W.[Wen],
Pedestrian detection via logistic multiple instance boosting,
ICIP08(1464-1467).
IEEE DOI
0810
BibRef
Zhang, J.G.[Jian-Guo],
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People detection in low-resolution video with non-stationary background,
IVC(27), No. 4, 3 March 2009, pp. 437-443.
Elsevier DOI
0804
Visual surveillance; People detection; Bayesian fusion;
Long-term motion; AdaBoost
BibRef
Zhang, J.G.[Jian-Guo],
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Action categorization with modified hidden conditional random field,
PR(43), No. 1, January 2010, pp. 197-203,.
Elsevier DOI
0909
Action recognition; Graph model; Hidden conditional random field;
Optimum learning
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Zhang, J.G.[Jian-Guo],
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Action categorization by structural probabilistic latent semantic
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CVIU(114), No. 8, August 2010, pp. 857-864.
Elsevier DOI
1007
Action categorization; pLSA; Structural pLSA; Local shape context
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Jo, Y.G.[Young-Gwan],
Nam, W.H.[Woon-Hyun],
Han, J.H.[Joon-Hee],
Pedestrian Segmentation From Uncalibrated Monocular Videos Using a
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SPLetters(16), No. 7, July 2009, pp. 604-607.
IEEE DOI
0905
See also Object handoff between uncalibrated views without planar ground assumption.
BibRef
Mucientes, M.[Manuel],
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People detection through quantified fuzzy temporal rules,
PR(43), No. 4, April 2010, pp. 1441-1453.
Elsevier DOI
1002
People detection; Spatio-temporal pattern; Fuzzy temporal rules;
Mobile robotics; Evolutionary algorithms
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Zeng, B.[Bobo],
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IEICE(E95-D), No. 7, July 2012, pp. 1979-1988.
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WWW Link.
1208
BibRef
Earlier: A1, A3, A2:
Fast Vision-Based Object Recognition Using Combined Integral Map,
CVS09(445-454).
Springer DOI
0910
BibRef
Cao, T.P.[Tam Phuong],
Deng, G.[Guang],
Mulligan, D.[David],
Implementation of real-time pedestrian detection on FPGA,
IVCNZ08(1-6).
IEEE DOI
0811
BibRef
Borges, P.V.K.,
Pedestrian Detection Based on Blob Motion Statistics,
CirSysVideo(23), No. 2, February 2013, pp. 224-235.
IEEE DOI
1301
BibRef
Vinicius, P.,
Borges, P.V.K.,
Blob Motion Statistics for Pedestrian Detection,
DICTA11(442-447).
IEEE DOI
1205
BibRef
Zhang, H.B.[Hong-Bo],
Li, S.Z.[Shao-Zi],
Su, S.Z.[Song-Zhi],
Chen, S.Y.[Shu-Yuan],
Selecting Effective and Discriminative Spatio-Temporal Interest Points
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IEICE(E96-D), No. 8, August 2013, pp. 1783-1792.
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1308
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Xie, Y.F.[Yao-Fang],
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A pedestrian classification method based on transfer learning,
IASP10(420-425).
IEEE DOI
1004
BibRef
Su, S.Z.[Song-Zhi],
Chen, S.Y.[Shu-Yuan],
Li, S.Z.[Shao-Zi],
Duh, D.J.[Der-Jyh],
Structured Local Edge Pattern Moment for pedestrian detection,
IASP10(556-560).
IEEE DOI
1004
BibRef
Negri, P.[Pablo],
Goussies, N.[Norberto],
Lotito, P.[Pablo],
Detecting pedestrians on a Movement Feature Space,
PR(47), No. 1, 2014, pp. 56-71.
Elsevier DOI
1310
BibRef
Earlier: A1, A3, Only:
Pedestrian Detection Using a Feature Space Based on Colored Level Lines,
CIARP12(885-892).
Springer DOI
1209
Pedestrian detection
BibRef
Negri, P.[Pablo],
Pedestrian Detection Using Multi-Objective Optimization,
CIARP15(776-784).
Springer DOI
1511
BibRef
Miseikis, J.,
Borges, P.V.K.,
Joint Human Detection From Static and Mobile Cameras,
ITS(16), No. 2, April 2015, pp. 1018-1029.
IEEE DOI
1504
Cameras
BibRef
Dohi, K.[Keisuke],
Negi, K.[Kazuhiro],
Shibata, Y.[Yuichiro],
Oguri, K.[Kiyoshi],
FPGA Implementation of Human Detection by HOG Features with AdaBoost,
IEICE(E96-D), No. 8, August 2013, pp. 1676-1684.
WWW Link.
1308
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Crooks, A.[Andrew],
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Lu, X.[Xu],
Wise, S.[Sarah],
Irvine, J.M.[John M.],
Stefanidis, A.[Anthony],
Walk This Way: Improving Pedestrian Agent-Based Models through Scene
Activity Analysis,
IJGI(4), No. 3, 2015, pp. 1627.
DOI Link
1511
BibRef
Bhole, C.[Chetan],
Pal, C.[Christopher],
Fully automatic person segmentation in unconstrained video using
spatio-temporal conditional random fields,
IVC(51), No. 1, 2016, pp. 58-68.
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1606
BibRef
Earlier:
Automated person segmentation in videos,
ICPR12(3672-3675).
WWW Link.
1302
Person segmentation
BibRef
Noaman, R.A.K.[Redwan A.K.],
Ali, M.A.M.[Mohd Alauddin Mohd],
Zainal, N.[Nasharuddin],
Enhancing pedestrian detection using optical flow for surveillance,
IJCVR(7), No. 1/2, 2017, pp. 35-48.
DOI Link
1701
BibRef
Wang, Y.[Yi],
Piérard, S.[Sébastien],
Su, S.Z.[Song-Zhi],
Jodoin, P.M.[Pierre-Marc],
Improving pedestrian detection using motion-guided filtering,
PRL(96), No. 1, 2017, pp. 106-112.
Elsevier DOI
1709
BibRef
Earlier:
Nonlinear Background Filter to Improve Pedestrian Detection,
SBMI15(535-543).
Springer DOI
1511
Pedestrian, detection
BibRef
Gavrilyuk, K.[Kirill],
Ghodrati, A.[Amir],
Li, Z.Y.[Zhen-Yang],
Snoek, C.G.M.[Cees G. M.],
Actor and Action Video Segmentation from a Sentence,
CVPR18(5958-5966)
IEEE DOI
1812
Visualization, Natural languages, Image segmentation, Training,
Convolutional neural networks, Vocabulary, Electron tubes
BibRef
Pei, C.,
Hao, L.,
Zhu, Y.,
Pedestrian detection with dynamic iterative bootstrapping,
ICIP17(4182-4186)
IEEE DOI
1803
Benchmark testing, Detectors, Feature extraction,
Iterative methods, Proposals, Standards, Training, Faster R-CNN,
region proposal network
BibRef
Yamaguchi, M.,
Saito, K.,
Ushiku, Y.,
Harada, T.,
Spatio-Temporal Person Retrieval via Natural Language Queries,
ICCV17(1462-1471)
IEEE DOI
1802
image motion analysis, image retrieval,
natural language processing, query processing, text analysis,
Visualization
BibRef
Wang, F.[Fei],
Lin, C.F.[Cai-Fang],
Huang, Q.[Qian],
Pedestrian Detection Over 100 fps with C4 Algorithm,
CVS17(499-506).
Springer DOI
1711
BibRef
Kilicarslan, M.,
Zheng, J.Y.,
Raptis, K.,
Pedestrain detection from motion,
ICPR16(1857-1863)
IEEE DOI
1705
Automobiles, Cameras, Dynamics, Image edge detection,
Legged locomotion, Shape, Trajectory, driving video,
pedestrian detection, pedestrian motion,
spatial-temporal filtering, tracking
BibRef
Seguin, G.[Guillaume],
Bojanowski, P.[Piotr],
Lajugie, R.[Rémi],
Laptev, I.[Ivan],
Instance-Level Video Segmentation from Object Tracks,
CVPR16(3678-3687)
IEEE DOI
1612
BibRef
Jeon, S.P.[Seong Pyo],
Lee, Y.S.[Yoon Suk],
Choi, K.N.[Kwang Nam],
Movement direction-based approaches for pedestrian detection in road
scenes,
FCV15(1-4)
IEEE DOI
1506
driver information systems
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González, A.[Alejandro],
Vázquez, D.[David],
Ramos, S.[Sebastian],
López, A.M.[Antonio M.],
Amores, J.[Jaume],
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection,
IbPRIA15(3-12).
Springer DOI
1506
BibRef
González, A.[Alejandro],
Villalonga, G.[Gabriel],
Ros, G.[German],
Vázquez, D.[David],
López, A.M.[Antonio M.],
3D-Guided Multiscale Sliding Window for Pedestrian Detection,
IbPRIA15(560-568).
Springer DOI
1506
BibRef
Hariyono, J.[Joko],
Jo, K.H.[Kang-Hyun],
Detection of pedestrian crossing road,
ICIP15(4585-4588)
IEEE DOI
1512
Pedestrian detection; location classification; optical flow
BibRef
Mao, Y.X.[Yun-Xiang],
Yin, Z.Z.[Zhao-Zheng],
Training a Scene-Specific Pedestrian Detector Using Tracklets,
WACV15(170-176)
IEEE DOI
1503
Cameras
BibRef
Zhu, J.J.[Jie-Jie],
Javed, O.[Omar],
Liu, J.G.[Jin-Gen],
Yu, Q.[Qian],
Cheng, H.[Hui],
Sawhney, H.[Harpreet],
Pedestrian Detection in Low-Resolution Imagery by Learning
Multi-scale Intrinsic Motion Structures (MIMS),
CVPR14(3510-3517)
IEEE DOI
1409
BibRef
Brown, L.M.[Lisa M.],
Feris, R.S.[Rogerio S.],
Pankanti, S.[Sharathchandra],
Temporal Non-maximum Suppression for Pedestrian Detection Using
Self-Calibration,
ICPR14(2239-2244)
IEEE DOI
1412
Accuracy
BibRef
Al Harbi, N.[Nouf],
Gotoh, Y.[Yoshihiko],
Spatio-temporal Human Body Segmentation from Video Stream,
CAIP13(78-85).
Springer DOI
1308
BibRef
Al Ghamdi, M.[Manal],
Gotoh, Y.[Yoshihiko],
Manifold Matching with Application to Instance Search Based on Video
Queries,
ICISP14(477-486).
Springer DOI
1406
BibRef
Earlier:
Spatio-temporal Manifold Embedding for Nearly-Repetitive Contents in a
Video Stream,
CAIP13(70-77).
Springer DOI
1308
BibRef
Al Ghamdi, M.[Manal],
Al Harbi, N.[Nouf],
Gotoh, Y.[Yoshihiko],
Spatio-temporal Video Representation with Locality-Constrained Linear
Coding,
ARTEMIS12(III: 101-110).
Springer DOI
1210
BibRef
Yang, Y.[Yang],
Shu, G.[Guang],
Shah, M.[Mubarak],
Semi-supervised Learning of Feature Hierarchies for Object Detection
in a Video,
CVPR13(1650-1657)
IEEE DOI
1309
Object detection; deep learning; feature learning; video analysis.
Learn video-specific features. Person and horse detection application.
BibRef
Hahnle, M.[Michael],
Saxen, F.[Frerk],
Hisung, M.[Matthias],
Brunsmann, U.[Ulrich],
Doll, K.[Konrad],
FPGA-Based Real-Time Pedestrian Detection on High-Resolution Images,
ECVW13(629-635)
IEEE DOI
1309
FPGA
BibRef
de Smedt, F.[Floris],
Hulens, D.[Dries],
Goedeme, T.[Toon],
On-board real-time tracking of pedestrians on a UAV,
ECVW15(1-8)
IEEE DOI
1510
Approximation methods
BibRef
de Smedt, F.[Floris],
van Beeck, K.[Kristof],
Tuytelaars, T.[Tinne],
Goedeme, T.[Toon],
Pedestrian Detection at Warp Speed: Exceeding 500 Detections per
Second,
ECVW13(622-628)
IEEE DOI
1309
GPU optimization;Object detection;Vision application
BibRef
Kawanishi, Y.,
Deguchi, D.[Daisuke],
Ide, I.[Ichiro],
Murase, H.[Hiroshi],
Fujiyoshi, H.,
Misclassification tolerable learning for robust pedestrian
orientation classification,
ICPR16(486-491)
IEEE DOI
1705
Accidents, Cameras, Legged locomotion, Optimization, Sensors,
Support vector machines, Training
BibRef
Wakayama, M.[Masashi],
Deguchi, D.[Daisuke],
Doman, K.[Keisuke],
Ide, I.[Ichiro],
Murase, H.[Hiroshi],
Tamatsu, Y.[Yukimasa],
Estimation of the human performance for pedestrian detectability based
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ICPR12(1940-1943).
WWW Link.
1302
BibRef
Jin, X.[Xuyuan],
Heinrich, A.[Adrienne],
Shan, C.F.[Cai-Feng],
de Haan, G.[Gerard],
Shared-bed person segmentation based on motion estimation,
ICIP12(137-140).
IEEE DOI
1302
BibRef
Dollár, P.[Piotr],
Appel, R.[Ron],
Kienzle, W.[Wolf],
Crosstalk Cascades for Frame-Rate Pedestrian Detection,
ECCV12(II: 645-659).
Springer DOI
1210
BibRef
Chen, W.[Wei],
Yang, Q.X.[Quan-Xi],
Lin, K.W.[Ke-Wei],
Wang, S.Y.[Sheng-Yu],
Huang, C.L.[Chung-Lin],
Human and car identification using motion vector in H.264 compressed
video,
VCIP11(1-4).
IEEE DOI
1201
Find moving objects, then classify.
BibRef
Munjal, B.[Bharti],
Amin, S.[Sikandar],
Tombari, F.[Federico],
Galasso, F.[Fabio],
Query-Guided End-To-End Person Search,
CVPR19(811-820).
IEEE DOI
2002
BibRef
del Bimbo, A.[Alberto],
Lisanti, G.[Giuseppe],
Masi, I.[Iacopo],
Pernici, F.[Federico],
Person Detection Using Temporal and Geometric Context with a Pan Tilt
Zoom Camera,
ICPR10(3886-3889).
IEEE DOI
1008
BibRef
Yamauchi, Y.J.[Yu-Ji],
Fujiyoshi, H.[Hironobu],
Hwang, B.W.[Bon-Woo],
Kanade, T.[Takeo],
People detection based on co-occurrence of appearance and
spatiotemporal features,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Venkatraman, D.,
Makur, A.,
Shadow-less segmentation of moving humans from surveillance video
sequences,
ICARCV08(1317-1322).
IEEE DOI
1109
BibRef
Beleznai, C.[Csaba],
Rauter, M.[Michael],
Shao, D.[Dan],
Real-time human detection using fast contour
template matching for visual surveillance,
AVSBS11(514).
IEEE DOI
1111
AVSS2011 demo session
BibRef
Kim, D.H.[Dae-Hwan],
Kim, D.J.[Dai-Jin],
Self-occlusion handling for human body motion tracking from 3D ToF
image sequence,
3DVP10(57-62).
DOI Link
1111
BibRef
Garcia-Martin, A.,
Hauptmann, A.G.,
Martinez, J.M.,
People detection based on appearance and motion models,
AVSBS11(256-260).
IEEE DOI
1111
BibRef
Mayya, M.[Mohammad],
Zarka, N.[Nizar],
Alkadi, M.S.[Mohammad Soubhi],
Embedded system for real-time human motion detection,
IPTA10(523-528).
IEEE DOI
1007
BibRef
Cheung, S.M.[Siu-Ming],
Moon, Y.S.[Yiu-Sang],
Detection of Approaching Pedestrians from a Distance Using Temporal
Intensity Patterns,
MVA09(354-).
PDF File.
0905
BibRef
Figueira, D.[Dario],
Moreno, P.[Plinio],
Bernardino, A.[Alexandre],
Gaspar, J.[José],
Santos-Victor, J.[José],
Optical Flow Based Detection in Mixed Human Robot Environments,
ISVC09(I: 223-232).
Springer DOI
0911
Optical flow features to distinguish between humans and robots.
See also Re-identification of Visual Targets in Camera Networks: A Comparison of Techniques.
BibRef
Ding, W.R.[Wen-Rui],
Li, H.G.[Hong-Guang],
Jiang, Z.[Zhe],
Li, X.J.[Xin-Jun],
Unsupervised Spatio-Temporal Multi-Human Detection and Recognition in
Complex Scene,
CISP09(1-5).
IEEE DOI
0910
BibRef
Habe, H.[Hitoshi],
Nakagawa, H.[Hidehito],
Kidode, M.[Masatsugu],
Efficient acquisition of human existence priors from motion
trajectories,
VCL-ViSU09(85-91).
IEEE DOI
0906
Person detection based on prior trajectories.
BibRef
Goel, D.[Dhiraj],
Chen, T.H.[Tsu-Han],
Pedestrian Detection Using Global-Local Motion Patterns,
ACCV07(I: 220-229).
Springer DOI
0711
BibRef
And:
Real-Time Pedestrian Detection using Eigenflow,
ICIP07(III: 229-232).
IEEE DOI
0709
BibRef
Dalal, N.[Navneet],
Triggs, B.[Bill],
Schmid, C.[Cordelia],
Human Detection Using Oriented Histograms of Flow and Appearance,
ECCV06(II: 428-441).
Springer DOI
0608
BibRef
Haga, T.,
Sumi, K.,
Yagi, Y.,
Human detection in outdoor scene using spatio-temporal motion analysis,
ICPR04(IV: 331-334).
IEEE DOI
0409
BibRef
Wöhler, C.,
Kreßel, U.,
Anlauf, J.K.,
Pedestrian Recognition by Classification of Image Sequences:
Global Approaches vs. Local Spatio-temporal Processing,
ICPR00(Vol II: 540-544).
IEEE DOI
0009
See also Real-time object recognition on image sequences with the adaptable time delay neural network algorithm applications for autonomous vehicles.
See also Time Delay Neural Network Algorithm for Estimating Image-Pattern Shape and Motion, A.
BibRef
Heisele, B.[Bernd],
Wöhler, C.[Christian],
Motion-Based Recognition of Pedestrians,
ICPR98(Vol II: 1325-1330).
IEEE DOI
9808
BibRef
Vannoorenberghe, P.[Patrick],
Motamed, C.[Cina],
Blosseville, J.M.[Jean-Marc],
Postaire, J.G.[Jack-Gérard],
Automatic pedestrian recognition using real-time motion analysis,
CIAP97(II: 493-500).
Springer DOI
9709
BibRef
Kinzel, W.,
Dickmanns, E.D.,
Moving Humans Recognition Using Spatio-Temporal Models,
ISPRS92(xx).
BibRef
9200
Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Detection, People Detection, Pedestrians, Using Depth, Stereo .