Publication 2004 Issue No. 9 - September Abstract - Tracking Multiple Humans in Complex Situations
Tracking Multiple Humans in Complex Situations
September 2004 (vol. 26 no. 9)
pp. 1208-1221
 ASCII Text x Tao Zhao, Ram Nevatia, "Tracking Multiple Humans in Complex Situations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1208-1221, September, 2004.
 BibTex x @article{ 10.1109/TPAMI.2004.73,author = {Tao Zhao and Ram Nevatia},title = {Tracking Multiple Humans in Complex Situations},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {26},number = {9},issn = {0162-8828},year = {2004},pages = {1208-1221},doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.73},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - Tracking Multiple Humans in Complex SituationsIS - 9SN - 0162-8828SP1208EP1221EPD - 1208-1221A1 - Tao Zhao, A1 - Ram Nevatia, PY - 2004KW - Multiple-human segmentationKW - multiple-human trackingKW - visual surveillanceKW - human shape modelKW - human locomotion model.VL - 26JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -
Tao Zhao, IEEE
Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences.

[1] A.M. Baumberg, Learning Deformable Models for Tracking Human Motion PhD thesis, Univ. of Leeds, 1995.
[2] G.A. Bekey, Walking The Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed., MIT press, 1995.
[3] M. Brand, “Shadow Puppetry,” Proc. Int'l Conf. Computer Vision, 1999.
[4] C. Bregler, “Learning and Recognizing Human Dynamics in Video Sequences,” Proc. Conf. Computer Vision and Pattern Recognition, 1997.
[5] A.F. Bobick and J.W. Davis, “The Recognition of Human Movement Using Temporal Templates,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 3, Mar. 2001.
[6] Character Studio: Software Package, http://www.discreet.com/productscs/, 2002.
[7] G. Medioni, Detecting and Tracking Moving Objects for Video Surveillance Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 319-325, 1999.
[8] R. Cutler and L.S. Davis, Robust Real-Time Periodic Motion Detection, Analysis, and Applications IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, Aug. 2000.
[9] J. Deutscher, A. Davison, and I. Reid, Automatic Partitioning of High Dimensional Search Spaces Associated with Articulated Body Motion Capture Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 669-676, 2001.
[10] A.A. Efros, A.C. Berg, G. Mori, and J. Malik, Recognizing Action at a Distance Proc. IEEE Int'l Conf. Computer Vision, pp. 726-733, 2003.
[11] A. Elgammal and L.S. Davis, Probabilistic Framework for Segmenting People under Occlusion Proc. IEEE Eighth Int'l Conf. Computer Vision, 2001.
[12] D. Forsyth and J. Ponce, Computer Vision: A Modern Approach. Prentice-Hall, 2001.
[13] S. Hongeng and R. Nevatia, Multi-Agent Event Recognition Proc. Int'l Conf. Computer Vision, vol. 2, pp. 84-91, 2001.
[14] I. Haritaoglu, D. Harwood, and L.S. Davis, W4S: A Real-Time System for Detecting and Tracking People in 2 1/2 D Proc. European Conf. Computer Vision, pp. 962-968, 1998.
[15] I. Haritaoglu, D. Harwood, and L. Davis, $\rm W^4$: Real-Time Surveillance of People and Their Activities IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug. 2000.
[16] R. Hartley and A. Zisserman, Multi View Geometry. Cambridge Press, 2000.
[17] M. Isard and A. Blake, Condensation-Conditional Density Propagation for Visual Tracking Int'l J. Computer Vision, vol. 29, no. 1, pp. 5-28, 1998.
[18] M. Isard and J. MacCormick, BraMBLe: A Bayesian Multiple-Blob Tracker Proc. Int'l Conf. Computer Vision, vol. 2, pp. 34-41, 2001.
[19] R. Kalman, A New Approach to Linear Filtering and Prediction Problems J. Basic Eng., vol. 82, pp. 35-45, 1960.
[20] P. Kornprobst and G. Medioni, Tracking Segmented Objects Using Tensor Voting Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 118-125, 2000.
[21] N. Krahnstover, M. Yeasin, and R. Sharma, Towards a Unified Framework for Tracking and Analysis of Human Motion Proc. IEEE Workshop Detection and Recognition of Events in Video, 2001.
[22] D. Liebowitz, A. Criminisi, and A. Zisserman, Creating Architectural Models from Images Proc. EUROGRAPH Conf., vol. 18, pp. 39-50, 1999.
[23] A.J. Lipton, H. Fujiyoshi, and R.S. Patil, Moving Target Classification and Tracking from Real-Time Video Proc. DARPA IU Workshop, pp. 129-136, 1998.
[24] F. Lv, T. Zhao, and R. Nevatia, Self-Calibration of a Camera from a Walking Human Proc. Int'l Conf. Pattern Recognition, vol. 1, pp. 562-567, 2002.
[25] S.J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler, Tracking Groups of People Computer Vision and Image Understanding, vol. 80, no. 1, pp. 42-56, 2000.
[26] T.B. Moeslund and E. Granum, A Survey of Computer Vision-Based Human Motion Capture Computer Vision and Image Understanding, vol. 81, pp. 231-268, 2001.
[27] G. Mori and J. Malik, Estimating Human Body Configurations Using Shape Context Matching Proc. European Conf. Computer Vision, pp. 666-681, 2002.
[28] R. Murry, Z.X. Li, and S. Sastry, A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.
[29] NOVAS Naval Observatory Vector Astrometry Subroutines, http://aa.usno.navy.mil/software/novasnovas_info.html , 2003.
[30] Data Set Provided by IEEE Workshop on Performance Evaluation of Tracking and Surveillance (PETS2001), 2001.
[31] S. Pingali and J. Segen, “Performance Evaluation of People Tracking System,” Proc. IEEE CS Workshop Applications in Computer Vision, pp. 33-38, Sarasota, Fla., 1996.
[32] P. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. Bowyer, The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm Proc. Int'l Conf. Pattern Recognition, 2002.
[33] A. Prati, R. Cucchiara, I. Mikic, and M.M. Trivedi, Analysis and Detection of Shadows in Video Streams: A Comparative Evaluation Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2001.
[34] L.R. Rabiner, “Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257-285, 1989.
[35] K. Rohr, Towards Model-Based Recognition of Human Movements in Image Sequences CVGIP: Image Understanding, vol. 59, no. 1, pp. 94-115, 1994.
[36] R. Rosales and S. Sclaroff, “3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 117-123, 1999.
[37] H. Sidenbladh, M.J. Black, and D.J. Fleet, Stochastic Tracking of 3D Human Figures Using 2D Image Motion Proc. European Conf. Computer Vision, pp. 702-718, 2000.
[38] N.T. Siebel and S. Maybank, Fusion of Multiple Tracking Algorithm for Robust People Tracking Proc. European Conf. Computer Vision, pp. 373-387, 2002.
[39] Y. Song, X. Feng, and P. Perona, Towards Detection of Human Motion Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 810-817, 2000.
[40] C. Stauffer and W.E.L. Grimson, Learning Patterns of Activity Using Real-Time Tracking IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug. 2000.
[41] H. Tao, H.S. Sawhney, and R. Kumar, A Sampling Algorithm for Tracking Multiple Objects Proc. IEEE Workshop Vision Algorithms, 1999.
[42] H. Tao, H.S. Sawhney, and R. Kumar, Object Tracking with Bayesian Estimation of Dynamic Layer Representations IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 75-89, Jan. 2002.
[43] A.M. Tekalp, Digitial Video Processing. Prentice Hall, 1995.
[44] C. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, Pfinder: Real-Time Tracking of the Human Body IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780-785, July 1997.
[45] T. Zhao, R. Nevatia, and F. Lv, Segmentation and Tracking of Multiple Humans in Complex Situations Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 194-201, 2001.
[46] T. Zhao and R. Nevatia, 3D Tracking of Human Locomotion: A Tracking as Recognition Approach Proc. Int'l Conf. Pattern Recognition, vol. 1, pp. 546-551, 2002.
[47] T. Zhao, Model-Based Segmentation and Tracking of Multiple Humans in Complex Situations PhD thesis, Univ. of Southern California, Los Angeles, 2003.

Index Terms:
Multiple-human segmentation, multiple-human tracking, visual surveillance, human shape model, human locomotion model.
Citation:
Tao Zhao, Ram Nevatia, "Tracking Multiple Humans in Complex Situations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1208-1221, Sept. 2004, doi:10.1109/TPAMI.2004.73