17.1.3.2.5 Human Detection, People Detection, Pedestrians, Using Depth, Stereo

Chapter Contents (Back)
Human Detection. Pedestrian Detection. Depth. RBG-D.
See also Human Detection, People Detection, Pedestrians, Locating.
See also Tracking People, Human Tracking, Pedestrian Tracking.

Zhao, L., Thorpe, C.E.,
Stereo- and neural network-based pedestrian detection,
ITS(1), No. 3, September 2000, pp. 148-154.
IEEE Abstract. 0402
BibRef

Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., Jung, H.G.,
A New Approach to Urban Pedestrian Detection for Automatic Braking,
ITS(10), No. 4, December 2009, pp. 594-605.
IEEE DOI 0912

See also tool for vision based pedestrian detection performance evaluation, A. BibRef

Bertozzi, M., Binelli, E., Broggi, A., del Rose, M.,
Stereo Vision-Based Approaches for Pedestrian Detection,
OTCBVS05(III: 16-16).
IEEE DOI 0507

See also Pedestrian detection by means of far-infrared stereo vision. BibRef

Oliveira, L.[Luciano], Nunes, U.[Urbano], Peixoto, P.[Paulo], Silva, M.[Marco], Moita, F.[Fernando],
Semantic fusion of laser and vision in pedestrian detection,
PR(43), No. 10, October 2010, pp. 3648-3659.
Elsevier DOI 1007
Semantic sensor fusion; Pedestrian detection; Markov logic network BibRef

Wang, Y.[Yu], Kato, J.[Jien],
Pedestrian Detection with Sparse Depth Estimation,
IEICE(E94-D), No. 8, August 2011, pp. 1690-1699.
WWW Link. 1108
BibRef

Enzweiler, M.[Markus], Gavrila, D.M.[Dariu M.],
A Multilevel Mixture-of-Experts Framework for Pedestrian Classification,
IP(20), No. 10, October 2011, pp. 2967-2979.
IEEE DOI 1110
BibRef

Enzweiler, M.[Markus], Eigenstetter, A.[Angela], Schiele, B.[Bernt], Gavrila, D.M.[Dariu M.],
Multi-cue pedestrian classification with partial occlusion handling,
CVPR10(990-997).
IEEE DOI 1006
BibRef

Rohrbach, M.[Marcus], Enzweiler, M.[Markus], Gavrila, D.M.[Dariu M.],
High-Level Fusion of Depth and Intensity for Pedestrian Classification,
DAGM09(101-110).
Springer DOI 0909
BibRef

Keller, C.G.[Christoph G.], Hermes, C.[Christoph], Gavrila, D.M.[Dariu M.],
Will the Pedestrian Cross? Probabilistic Path Prediction Based on Learned Motion Features,
DAGM11(386-395).
Springer DOI 1109
Award, GCPR, HM. BibRef

Keller, C.G.[Christoph Gustav], Fernández Llorca, D.[David], Gavrila, D.M.[Dariu M.],
Dense Stereo-Based ROI Generation for Pedestrian Detection,
DAGM09(81-90).
Springer DOI 0909
BibRef

Keller, C.G., Enzweiler, M., Rohrbach, M., Llorca, D.F., Schnorr, C., Gavrila, D.M.,
The Benefits of Dense Stereo for Pedestrian Detection,
ITS(12), No. 4, December 2011, pp. 1096-1106.
IEEE DOI 1112
BibRef

Corneliu, T., Nedevschi, S.[Sergiu],
Real-time pedestrian classification exploiting 2D and 3D information,
IET-ITS(2), No. 3, 2008, pp. 201-210.
DOI Link 1204
BibRef

Bota, S., Nedevschi, S.[Sergiu],
Multi-feature walking pedestrians detection for driving assistance systems,
IET-ITS(2), No. 2, 2008, pp. 92-104.
DOI Link 1204
BibRef

Ye, Q.X.[Qi-Xiang], Liang, J.X.[Ji-Xiang], Jiao, J.B.[Jian-Bin],
Pedestrian Detection in Video Images via Error Correcting Output Code Classification of Manifold Subclasses,
ITS(13), No. 1, March 2012, pp. 193-202.
IEEE DOI 1203
BibRef

Wu, B.[Bo], Liang, J.X.[Ji-Xiang], Ye, Q.X.[Qi-Xiang], Han, Z.J.[Zhen-Jun], Jiao, J.B.[Jian-Bin],
Fast Pedestrian Detection with Laser and Image Data Fusion,
ICIG11(605-608).
IEEE DOI 1109
BibRef

Hou, Y.L.[Ya-Li], Pang, G.K.H.[Grantham K. H.],
Multicue-Based Crowd Segmentation Using Appearance and Motion,
SMCS(43), No. 2, March 2013, pp. 356-369.
IEEE DOI 1303
BibRef
Earlier:
Multi-cue-Based Crowd Segmentation in Stereo Vision,
CAIP11(I: 93-101).
Springer DOI 1109
BibRef

Zhang, H., Reardon, C., Parker, L.E.,
Real-Time Multiple Human Perception With Color-Depth Cameras on a Mobile Robot,
Cyber(43), No. 5, 2013, pp. 1429-1441.
IEEE DOI 1309
Cameras BibRef

Kiss, Á.[Ákos], Szirányi, T.[Tamás],
Localizing people in multi-view environment using height map reconstruction in real-time,
PRL(34), No. 16, 2013, pp. 2135-2143.
Elsevier DOI 1310
Multi-view localization BibRef

Baltieri, D.[Davide], Vezzani, R.[Roberto], Cucchiara, R.[Rita], Utasi, A.[Akos], Benedek, C.[Csaba], Sziranyi, T.[Tamas],
Multi-view people surveillance using 3D information,
VS11(1817-1824).
IEEE DOI 1201
BibRef

van Oosterhout, T.[Tim], Englebienne, G.[Gwenn], Kröse, B.J.A.[Ben J.A.],
RARE: people detection in crowded passages by range image reconstruction,
MVA(26), No. 5, July 2015, pp. 561-573.
WWW Link. 1506
BibRef

Liu, J.[Jun], Zhang, G.[Guyue], Liu, Y.[Ye], Tian, L.C.[Lu-Chao], Chen, Y.Q.[Yan Qiu],
An ultra-fast human detection method for color-depth camera,
JVCIR(31), No. 1, 2015, pp. 177-185.
Elsevier DOI 1508
Human detection BibRef

Zhang, G.[Guyue], Liu, J.[Jun], Liu, Y.[Ye], Zhao, J.W.[Jing-Wen], Tian, L.[Luchao], Chen, Y.Q.[Yan Qiu],
Physical blob detector and Multi-Channel Color Shape Descriptor for human detection,
JVCIR(52), 2018, pp. 13-23.
Elsevier DOI 1804
Human detection, RGB-D camera, Physical blob detector, Multi-Channel Color Shape Descriptor BibRef

Zhao, X.H.[Xiao-Hui], Jiang, Y.C.[Yi-Cheng], Stathaki, T.[Tania],
A novel low false alarm rate pedestrian detection framework based on single depth images,
IVC(45), No. 1, 2016, pp. 11-21.
Elsevier DOI 1601
Pedestrian detection BibRef

Li, K., Wang, X., Xu, Y., Wang, J.,
Density Enhancement-Based Long-Range Pedestrian Detection Using 3-D Range Data,
ITS(17), No. 5, May 2016, pp. 1368-1380.
IEEE DOI 1605
Detectors BibRef

Zhang, Z.G.[Zhi-Guo], Tao, W.B.[Wen-Bing], Sun, K.[Kun], Hu, W.B.[Wen-Bin], Yao, L.[Li],
Pedestrian detection aided by fusion of binocular information,
PR(60), No. 1, 2016, pp. 227-238.
Elsevier DOI 1609
Pedestrian detection BibRef

Zhang, Z.G.[Zhi-Guo], Tao, W.B.[Wen-Bing],
Pedestrian Detection in Binocular Stereo Sequence Based on Appearance Consistency,
CirSysVideo(26), No. 9, September 2016, pp. 1772-1785.
IEEE DOI 1609
Computational modeling BibRef

Wang, H.Y.[Hui-Yan], Yan, Y.X.[Yi-Xiang], Hua, J.[Jing], Yang, Y.T.[Yu-Tao], Wang, X.[Xun], Li, X.L.[Xiao-Lan], Deller, J.R.[John Robert], Zhang, G.F.[Guo-Feng], Bao, H.J.[Hu-Jun],
Pedestrian recognition in multi-camera networks using multilevel important salient feature and multicategory incremental learning,
PR(67), No. 1, 2017, pp. 340-352.
Elsevier DOI 1704
Video pedestrian recognition BibRef

Kocur, D.[Dusan], Novak, D.[Daniel], Svecova, M.[Maria],
UWB Radar Signal Processing for Localization of Persons with the Changing Nature of Their Movement,
Sensors(207), No. 12, December 2016, pp. 50-57.
HTML Version. 1705
BibRef

Fan, Y.Y.[Yang-Yu], Du, R.[Rui], Wang, J.S.[Jian-Shu],
Identification of Pedestrian and Bicyclist through Range Micro Doppler Signatures,
IEICE(E101-D), No. 2, February 2018, pp. 552-555.
WWW Link. 1802
BibRef

Tian, L., Li, M., Hao, Y., Liu, J., Zhang, G., Chen, Y.Q.,
Robust 3-D Human Detection in Complex Environments With a Depth Camera,
MultMed(20), No. 9, September 2018, pp. 2249-2261.
IEEE DOI 1809
cameras, learning (artificial intelligence), neural nets, object detection, candidate head-top locating stage, deep learning BibRef

Wang, P.[Pichao], Li, W.Q.[Wan-Qing], Ogunbona, P.[Philip], Wan, J.[Jun], Escalera, S.[Sergio],
RGB-D-based human motion recognition with deep learning: A survey,
CVIU(171), 2018, pp. 118-139.
Elsevier DOI 1812
Human motion recognition, RGB-D data, Deep learning, Survey 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

Jia, Y.[Yong], Guo, Y.[Yong], Yan, C.[Chao], Sheng, H.X.[Hao-Xuan], Cui, G.L.[Guo-Long], Zhong, X.L.[Xiao-Ling],
Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Li, J.J.[Jiao-Jiao], Wu, J.J.[Jia-Ji], You, Y.[Yang], Jeon, G.G.[Gwang-Gil],
Parallel binocular stereo-vision-based GPU accelerated pedestrian detection and distance computation,
RealTimeIP(17), No. 3, June 2020, pp. 447-457.
Springer DOI 2006
BibRef

Pinho Ferraz, P.A.[Pedro Augusto], Godinho de Oliveira, B.A.[Bernardo Augusto], Freitas Ferreira, F.M.[Flávia Magalhăes], Paiva da Silva Martins, C.A.[Carlos Augusto],
Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision,
IET-ITS(14), No. 10, October 2020, pp. 1319-1327.
DOI Link 2009
BibRef

Fang, Z., López, A.M.,
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation,
ITS(21), No. 11, November 2020, pp. 4773-4783.
IEEE DOI 2011
Task analysis, Pose estimation, Roads, Legged locomotion, Skeleton, Videos, Autonomous driving, ADAS, cyclists intention recognition BibRef

Yan, Y.[Yuyao], Xu, M.[Ming], Smith, J.S.[Jeremy S.], Shen, M.[Mo], Xi, J.[Jin],
Multicamera pedestrian detection using logic minimization,
PR(112), 2021, pp. 107703.
Elsevier DOI 2102
Pedestrian detection, Multicamera, Homography, Logic minimization, Video surveillance BibRef

Peng, X.Y.[Xiao-Yi], Shan, J.[Jie],
Detection and Tracking of Pedestrians Using Doppler LiDAR,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Gao, X.[Xin], Xiong, Y.J.[Yi-Jin], Zhang, G.Y.[Guo-Ying], Deng, H.[Hui], Kou, K.K.[Kang-Kang],
Exploiting key points supervision and grouped feature fusion for multiview pedestrian detection,
PR(131), 2022, pp. 108866.
Elsevier DOI 2208
Multiview aggregation, Pedestrian detection, Key points, Grouped feature fusion BibRef

Katircioglu, I.[Isinsu], Rhodin, H.[Helge], Constantin, V.[Victor], Spörri, J.[Jörg], Salzmann, M.[Mathieu], Fua, P.[Pascal],
Self-Supervised Human Detection and Segmentation via Background Inpainting,
PAMI(44), No. 12, December 2022, pp. 9574-9588.
IEEE DOI 2212
BibRef
Earlier: A1, A2, A4, A5, A6, Only:
Human Detection and Segmentation via Multi-view Consensus,
ICCV21(2835-2844)
IEEE DOI 2203
Image segmentation, Training, Cameras, Optical imaging, Proposals, Optical sensors, Object detection, Self-supervised training, image inpainting. Location awareness, Geometry, Image segmentation, Motion segmentation, Training data, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Dai, X.B.[Xiao-Biao], Hu, J.P.[Jun-Ping], Luo, C.L.[Chun-Lei], Zerfa, H.[Houcine], Zhang, H.[Hai], Duan, Y.X.[Yu-Xia],
NIRPed: A Novel Benchmark for Nighttime Pedestrian and Its Distance Joint Detection,
ITS(24), No. 7, July 2023, pp. 6932-6942.
IEEE DOI 2307
Laser radar, Narrowband, Imaging, Cameras, Roads, Object detection, Lighting, Distance detection, faster-RCNN, pedestrian dataset BibRef


Pei, Y.F.[Yi-Fei], Shi, Z.P.[Zhi-Ping], Geng, Q.[Qichuan], Wang, Z.F.[Zhao-Fa], Zhang, Y.K.[Yong-Kang], Jiang, N.[Na],
RDEPD: Re-Exploring Depth Estimation for Pedestrian Detection,
ICIP23(2380-2384)
IEEE DOI 2312
BibRef

Lee, W.Y.[Wei-Yu], Jovanov, L.[Ljubomir], Philips, W.[Wilfried],
Cross-modality Attention and Multimodal Fusion Transformer for Pedestrian Detection,
RealWorld22(608-623).
Springer DOI 2304
BibRef

Lee, W.Y.[Wei-Yu], Jovanov, L.[Ljubomir], Philips, W.[Wilfried],
Multi-view Target Transformation for Pedestrian Detection,
RealWorld23(1-10)
IEEE DOI 2302
Learning systems, Limiting, Target recognition, Conferences, Network architecture, Distortion, Feature extraction BibRef

Vora, J.[Jeet], Dutta, S.[Swetanjal], Jain, K.[Kanishk], Karthik, S.[Shyamgopal], Gandhi, V.[Vineet],
Bringing Generalization to Deep Multi-View Pedestrian Detection,
RealWorld23(110-119)
IEEE DOI 2302
Training, Deep learning, Codes, Conferences, Benchmark testing BibRef

Qiu, R.[Rui], Xu, M.[Ming], Yan, Y.[Yuyao], Smith, J.S.[Jeremy S.], Yang, X.[Xi],
3D Random Occlusion and Multi-layer Projection for Deep Multi-camera Pedestrian Localization,
ECCV22(X:695-710).
Springer DOI 2211
BibRef

Song, L.C.[Liang-Chen], Wu, J.[Jialian], Yang, M.[Ming], Zhang, Q.[Qian], Li, Y.[Yuan], Yuan, J.S.[Jun-Song],
Stacked Homography Transformations for Multi-View Pedestrian Detection,
ICCV21(6029-6037)
IEEE DOI 2203
Benchmark testing, Cameras, Task analysis, Standards, Stereo, 3D from multiview and other sensors, Detection and localization in 2D and 3D BibRef

Li, T.H.[Tian-Hong], Fan, L.J.[Li-Jie], Yuan, Y.[Yuan], Katabi, D.[Dina],
Unsupervised Learning for Human Sensing Using Radio Signals,
WACV22(1091-1100)
IEEE DOI 2202
Radio frequency, Representation learning, RF signals, Supervised learning, Sensors, Trajectory, Transfer, Semi- and Un- supervised Learning BibRef

Lima, J.P.[Joăo Paulo], Roberto, R.[Rafael], Figueiredo, L.[Lucas], Simőes, F.[Francisco], Teichrieb, V.[Veronica],
Generalizable Multi-Camera 3D Pedestrian Detection,
LXCV21(1232-1240)
IEEE DOI 2109
Training, Sensitivity, Fuses, Pose estimation BibRef

Zhong, T.[Tao], Kim, W.[Wonjik], Tanaka, M.[Masayuki], Okutomi, M.[Masatoshi],
Human Segmentation with Dynamic LiDAR Data,
ICPR21(1166-1172)
IEEE DOI 2105
Image segmentation, Laser radar, Motion segmentation, Dynamics, Neural networks, Estimation BibRef

Zhao, Z., Zhang, J., Shan, S.,
Noise Robust Hard Example Mining for Human Detection with Efficient Depth-Thermal Fusion,
FG20(809-813)
IEEE DOI 2102
Noise robustness, Feature extraction, Training, Head, Thermal noise, Kernel, Detectors BibRef

Tupper, A., Green, R.,
Pedestrian Proximity Detection using RGB-D Data,
IVCNZ19(1-6)
IEEE DOI 2004
cameras, edge detection, image capture, image colour analysis, image filtering, image matching, image segmentation, Intel RealSense D435 stereo vision depth camera BibRef

Wang, F., Zhou, S., Panev, S., Han, J., Huang, D.,
Person-in-WiFi: Fine-Grained Person Perception Using WiFi,
ICCV19(5451-5460)
IEEE DOI 2004
image segmentation, learning (artificial intelligence), neural nets, pose estimation, receiving antennas, signal detection, Laser radar 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, image sensors, object detection, object tracking, optimisation, Magnetic heads BibRef

Lin, T., Tan, D.S., Tang, H., Chien, S., Chang, F., Chen, Y., Cheng, W., Hua, K.,
Pedestrian Detection from Lidar Data via Cooperative Deep and Hand-Crafted Features,
ICIP18(1922-1926)
IEEE DOI 1809
Feature extraction, Laser radar, Sensors, deep learning 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

Matti, D., Ekenel, H.K., Thiran, J.P.,
Combining LiDAR space clustering and convolutional neural networks for pedestrian detection,
AVSS17(1-6)
IEEE DOI 1806
feature extraction, image classification, neural nets, object detection, optical radar, pattern clustering, pedestrians, Visualization BibRef

Santoso, P.S., Hang, H.M.,
Learning-based human detection applied to RGB-D images,
ICIP17(3365-3369)
IEEE DOI 1803
Feature extraction, Principal component analysis, Proposals, Robustness, Sensors, Support vector machines, Training, CNNs, depth map BibRef

Li, H., Liu, J., Zhang, G., Gao, Y., Wu, Y.,
Multi-glimpse LSTM with color-depth feature fusion for human detection,
ICIP17(905-909)
IEEE DOI 1803
Context modeling, Feature extraction, Fuses, Image color analysis, Logic gates, Proposals, Training, Feature fusion, Human detection, RGB-D BibRef

Park, K., Kim, S., Sohn, K.,
Pedestrian proposal generation using depth-aware scale estimation,
ICIP17(2045-2049)
IEEE DOI 1803
Cameras, Estimation, Feature extraction, Image color analysis, Proposals, Radio frequency, Training, RGB-D object proposal, scale-invariant feature BibRef

Zhou, K., Paiement, A., Mirmehdi, M.,
Detecting humans in RGB-D data with CNNs,
MVA17(306-309)
DOI Link 1708
Cameras, Color, Encoding, Feature extraction, Image color analysis, Proposals BibRef

Candido, J.[Jorge], Marengoni, M.[Mauricio],
Ground Plane Segmentation Using Artificial Neural Network for Pedestrian Detection,
ICIAR17(268-277).
Springer DOI 1706
BibRef

Haque, A.[Albert], Alahi, A.[Alexandre], Fei-Fei, L.[Li],
Recurrent Attention Models for Depth-Based Person Identification,
CVPR16(1229-1238)
IEEE DOI 1612
BibRef

Hu, Z., Ai, H., Ren, H., Zhang, Y.,
Fast human detection in RGB-D images based on color-depth joint feature learning,
ICIP16(266-270)
IEEE DOI 1610
Cameras BibRef

Barsi, A., Lovas, T., Molnar, B., Somogyi, A., Igazvolgyi, Z.,
Pedestrian Detection By Laser Scanning And Depth Imagery,
ISPRS16(B3: 465-468).
DOI Link 1610
BibRef

Lin, Y.S., Chen, H.T., Chuang, J.H.,
An efficient probabilistic occupancy map-based people localization approach,
VCIP15(1-4)
IEEE DOI 1605
Cameras BibRef

Zarshenas, A.[Amin], Mesmakhosroshahi, M.[Maral], Kim, J.[Joohee],
Fast depth estimation using spatio-temporal prediction for stereo-based pedestrian detection,
VCIP15(1-4)
IEEE DOI 1605
Advanced driver assistance systems BibRef

Mesmakhosroshahi, M.[Maral], Kim, J.[Joohee], Lee, Y.[Yunsik], Kim, J.B.[Jong-Bok],
Stereo based region of interest generation for pedestrian detection in driver assistance systems,
ICIP13(3386-3389)
IEEE DOI 1402
Advanced driver assistance system BibRef

Mesmakhosroshahi, M.[Maral], Kim, J.[Joohee],
Reducing search space for fast pedestrian detection,
VCIP15(1-4)
IEEE DOI 1605
Correlation BibRef

Yi, J.[Jaeho], Lee, S.K.[Seung-Kyu], Bae, S.[Sujung], Jeong, M.[Moonsik],
Human Body Volume Recovery from Single Depth Image,
ISVC15(I: 396-405).
Springer DOI 1601
BibRef

Mehmood, M.O., Ambellouis, S., Achard, C.,
Exploiting 3D geometric primitives for multicamera pedestrian detection,
AVSS15(1-6)
IEEE DOI 1511
computational geometry BibRef

Seib, V.[Viktor], Schmidt, G.[Guido], Kusenbach, M.[Michael], Paulus, D.[Dietrich],
Fourier Features For Person Detection in Depth Data,
CAIP15(I:824-836).
Springer DOI 1511
BibRef

Ubukata, T.[Tom], Shibata, M.[Masatoshi], Terabayashi, K.[Kenji], Mora, A.[Alessandro], Kawashita, T.[Takehiro], Masuyama, G.[Gakuto], Umeda, K.[Kazunori],
Fast Human Detection Combining Range Image Segmentation and Local Feature Based Detection,
ICPR14(4281-4286)
IEEE DOI 1412
Cameras BibRef

Hoegner, L., Hanel, A., Weinmann, M., Jutzi, B., Hinz, S., Stilla, U.,
Towards people detection from fused time-of-flight and thermal infrared images,
PCV14(121-126).
DOI Link 1404
BibRef

Rodríguez González, D.I.[Domingo Iván], Hayet, J.B.[Jean-Bernard],
Fast Human Detection in RGB-D Images with Progressive SVM-Classification,
PSIVT13(337-348).
Springer DOI 1402
BibRef

Liu, J.[Jun], Liu, Y.[Ye], Cui, Y.[Ying], Chen, Y.Q.[Yan Qiu],
Real-time human detection and tracking in complex environments using single RGBD camera,
ICIP13(3088-3092)
IEEE DOI 1402
Human detection;RGBD;Tracking BibRef

Shen, Y.J.[Yu-Jie], Hao, Z.H.[Zhong-Hua], Wang, P.F.[Peng-Fei], Ma, S.W.[Shi-Wei], Liu, W.Q.[Wan-Quan],
A Novel Human Detection Approach Based on Depth Map via Kinect,
HAU3D13(535-541)
IEEE DOI 1309
BibRef

Rauter, M.[Michael],
Reliable Human Detection and Tracking in Top-View Depth Images,
HAU3D13(529-534)
IEEE DOI 1309
3D Vision
See also GPU-Accelerated Human Detection Using Fast Directional Chamfer Matching. BibRef

Won, K.H.[Kwang Hee], Gurmu, S.[Sisay], Jung, S.K.[Soon Ki],
Pedestrian detection using labeled depth data,
FCV13(117-120).
IEEE DOI 1304
BibRef

Migniot, C.[Cyrille], Ababsa, F.[Fakhreddine],
3D Human Tracking from Depth Cue in a Buying Behavior Analysis Context,
CAIP13(482-489).
Springer DOI 1308
BibRef

Liu, W.[Wu], Xia, T.[Tian], Wan, J.[Ji], Zhang, Y.D.[Yong-Dong], Li, J.T.[Jin-Tao],
RGB-D Based Multi-attribute People Search in Intelligent Visual Surveillance,
MMMod12(750-760).
Springer DOI 1201
BibRef

Ikemura, S.[Sho], Fujiyoshi, H.[Hironobu],
Human detection by Haar-like filtering using depth information,
ICPR12(813-816).
WWW Link. 1302
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

Martelli, S.[Samuele], San Biagio, M., Murino, V.[Vittorio],
Latent subcategory models for pedestrian detection with partial occlusion handling,
AVSS15(1-6)
IEEE DOI 1511
feature extraction BibRef

Martelli, S.[Samuele], Cristani, M.[Marco], Murino, V.[Vittorio],
Stereo-Based Framework for Pedestrian Detection with Partial Occlusion Handling,
AVSS12(25-30).
IEEE DOI 1211
BibRef

Cui, Y.[Yan], Chang, W.[Will], Nöll, T.[Tobias], Stricker, D.[Didier],
KinectAvatar: Fully Automatic Body Capture Using a Single Kinect,
CDF12(II:133-147).
Springer DOI 1304
BibRef

Mandeljc, R.[Rok], Kovac(ic(, S.[Stanislav], Kristan, M.[Matej], Perš, J.[Janez],
Non-sequential Multi-view Detection, Localization and Identification of People Using Multi-modal Feature Maps,
ACCV12(III:691-704).
Springer DOI 1304
BibRef

Sheasby, G.[Glenn], Valentin, J.[Julien], Crook, N.[Nigel], Torr, P.H.S.[Philip H.S.],
A Robust Stereo Prior for Human Segmentation,
ACCV12(II:94-107).
Springer DOI 1304
BibRef

Wang, N.[Ningbo], Gong, X.J.[Xiao-Jin], Liu, J.L.[Ji-Lin],
A new depth descriptor for pedestrian detection in RGB-D images,
ICPR12(3688-3691).
WWW Link. 1302
BibRef

Gu, D.G.[Dong-Ge], Zhao, Y.[Yong], Yuan, Y.[Yule], Hu, G.[Gang],
Human segmentation based on disparity map and GrabCut,
CVRS12(67-71).
IEEE DOI 1302
BibRef

Yu, S.Q.[Shi-Qi], Wu, S.Y.[Sheng-Yin], Wang, L.[Liang],
SLTP: A Fast Descriptor for People Detection in Depth Images,
AVSS12(43-47).
IEEE DOI 1211
BibRef

Borjas, V.[Víctor], Drozdzal, M.[Michal], Radeva, P.I.[Petia I.], Vitriŕ, J.[Jordi],
Human Relative Position Detection Based on Mutual Occlusion,
CIARP12(332-339).
Springer DOI 1209
BibRef

Lee, J.T.[Jong Taek], Chen, C.C.[Chia-Chih], Aggarwal, J.K.,
Recognizing human-vehicle interactions from aerial video without training,
WAVP11(53-60).
IEEE DOI 1106
BibRef

Xia, L.[Lu], Chen, C.C.[Chia-Chih], Aggarwal, J.K.,
View invariant human action recognition using histograms of 3D joints,
HAU3D12(20-27).
IEEE DOI 1207
BibRef
And:
Human detection using depth information by Kinect,
HAU3D11(15-22).
IEEE DOI 1106
BibRef

Ryoo, M.S., Lee, J.T.[Jong Taek], Aggarwal, J.K.,
Video scene analysis of interactions between humans and vehicles using event context,
CIVR10(462-469).
DOI Link 1007
BibRef
Earlier: A2, A1, A3:
View independent recognition of human-vehicle interactions using 3-D models,
WMVC09(1-8).
IEEE DOI 0912
BibRef

Zeng, C.B.[Cheng-Bin], Ma, H.D.[Hua-Dong],
Human detection using multi-camera and 3D scene knowledge,
ICIP11(1793-1796).
IEEE DOI 1201
BibRef

Cho, S.H.[Sang-Ho], Kim, D.[Daehwan], Kim, T.[Taewan], Kim, D.J.[Dai-Jin],
Pose robust human detection using multiple oriented 2d elliptical filters,
VNBA08(9-16).
DOI Link 1208
multiple oriented 2D elliptical filters (MO2DEFs), BibRef

Gill, T., Keller, J.M.[James M.], Anderson, D.T.[Derek T.], Luke, R.H.[Robert H.],
A system for change detection and human recognition in voxel space using the Microsoft Kinect sensor,
AIPR11(1-8).
IEEE DOI 1204
BibRef

Bombini, L.[Luca], Broggi, A.[Alberto], Buzzoni, M.[Michele], Medici, P.[Paolo],
Intelligent Overhead Sensor for Sliding Doors: A Stereo Based Method for Augmented Efficiency,
CIAP11(I: 217-226).
Springer DOI 1109
BibRef

Nam, B.[Bodam], Kang, S.I.[Sung-Il], Hong, H.K.[Hyun-Ki],
Pedestrian detection system based on stereo vision for mobile robot,
FCV11(1-7).
IEEE DOI 1102
BibRef

Ikemura, S.[Sho], Fujiyoshi, H.[Hironobu],
Real-Time Human Detection Using Relational Depth Similarity Features,
ACCV10(IV: 25-38).
Springer DOI 1011
BibRef

Hosotani, D.[Daisuke], Yoda, I.[Ikushi], Sakaue, K.[Katsuhiko],
Wheelchair recognition by using stereo vision and histogram of oriented gradients (HOG) in real environments,
WACV09(1-6).
IEEE DOI 0912
BibRef

Seki, A.[Akihito], Hattori, H.[Hiroshi], Nishiyama, M.[Manabu], Watanabe, T.[Tomoki],
Temporal integration for on-board stereo-based pedestrian detection,
WACV09(1-6).
IEEE DOI 0912
BibRef

Hattori, H.[Hiroshi], Seki, A.[Akihito], Nishiyama, M.[Manabu], Watanabe, T.[Tomoki],
Stereo-Based Pedestrian Detection using Multiple Patterns,
BMVC09(xx-yy).
PDF File. 0909

See also Real-time 3D tracking using multiple sample points. BibRef

Walk, S.[Stefan], Schindler, K.[Konrad], Schiele, B.[Bernt],
Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo,
ECCV10(VI: 182-195).
Springer DOI 1009
BibRef

Walk, S.[Stefan], Majer, N.[Nikodem], Schindler, K.[Konrad], Schiele, B.[Bernt],
New features and insights for pedestrian detection,
CVPR10(1030-1037).
IEEE DOI 1006
BibRef

Wojek, C.[Christian], Walk, S.[Stefan], Schiele, B.[Bernt],
Multi-cue onboard pedestrian detection,
CVPR09(794-801).
IEEE DOI 0906
BibRef

Sun, L.[Luo], Di, H.J.[Hui-Jun], Tao, L.M.[Lin-Mi], Xu, G.Y.[Guang-You],
A Robust Approach for Person Localization in Multi-camera Environment,
ICPR10(4036-4039).
IEEE DOI 1008
BibRef

Bansal, M.[Mayank], Matei, B.C.[Bogdan C.], Sawhney, H.S.[Harpreet S.], Jung, S.H.[Sang-Hack], Eledath, J.[Jayan],
Pedestrian detection with depth-guided structure labeling,
S3DV09(31-38).
IEEE DOI 0910
BibRef

Li, Y.[Yuan], Wu, B.[Bo], Nevatia, R.[Ram],
Human detection by searching in 3d space using camera and scene knowledge,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Bahadori, S., Iocchi, L., Nardi, D., Settembre, G.P.,
Stereo vision based human body detection from a localized mobile robot,
AVSBS05(499-504).
IEEE DOI 0602
BibRef

Li, L.Y.[Li-Yuan], Koh, Y.T.[Ying Ting], Ge, S.S.[Shuzhi Sam], Huang, W.M.[Wei-Min],
Stereo-based human detection for mobile service robots,
ICARCV04(I: 74-79).
IEEE DOI 0412
BibRef

Kruse, F., Folster, F., Ahrholdt, M., Rohling, H., Meinecke, M.M., To, T.B.,
Target classification based on near-distance radar sensors,
IVS04(722-727).
IEEE DOI 0411
E.g. detect petestrians. BibRef

Munkelt, O.[Olaf], Ridder, C.[Christof], Hansel, D.[David], Hafner, W.[Walter],
A Model Driven 3D Image Interpretation System Applied to Person Detection in Video Images,
ICPR98(Vol I: 70-73).
IEEE DOI 9808
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Detection, People Detection, Pedestrians, Using Body Parts, Body Shape .


Last update:Mar 16, 2024 at 20:36:19