|  Junejo I N, Foroosh H. Trajectory rectification and path modeling for video surveillance. In Proc. the 11th Interna-tional Conference on Computer Vision, October 2007. Makris D, Ellis T. Learning semantic scene models from observing activity in visual surveillance. IEEE Trans. Sys-tems, Man, and Cybernetics, Part B: Cybernetics, 2005, 35(3): 397-408. Wang X, Tieu K, Grimson W E L. Correspondence-free activity analysis and scene modeling in multiple camera views. IEEE Trans. Pattern Analysis and Machine Intelligence, 2010, 32(1): 56-71. Hu W, Li X, Tian G, Maybank S, Zhang Z. An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 2013, 35(5): 1051-1065. Morris B, Trivedi M. Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Analysis and Machine In-telligence, 2011, 33(11): 2287-2301. Yan W, Forsyth D A. Learning the behavior of users in a public space through video tracking. In Proc. the 7th IEEE Workshops on Application of Computer Vision, January 2005, pp.370-377. Ma S G, Wang W Q. Effectively discriminating fighting shots in action movies. Journal of Computer Science and Technology, 2011, 26(1): 187-194. Zhang T, Lu H, Li S Z. Learning semantic scene models by object classification and trajectory clustering. In Proc. IEEE Conference on Computer Vision and Pattern Recog-nition, June 2009, pp.1940-1947. Chen K W, Lai C C, Lee P J, Chen C S, Hung Y P. Adaptive learning for target tracking and true linking discovering across multiple non-overlapping cameras. IEEE Trans. Multimedia, 2011, 13(4): 625-638. Javed O, Shafique K, Rasheed Z, Shah M. Modeling intercamera space-time and appearance relationships for tracking across non-overlapping views. Computer Vision and Im-age Understanding, 2008, 109(2):146-162. Lucas B, Kanade T. An iterative image registration technique with an application to stereo vision. In Proc. IJCAI, Aug. 1981, pp.674-679. Chen X L, Yang L. Towards monitoring human activities using an omnidirectional camera. In Proc. the 4th IEEE In-ternational Conference on Multimodal Interfaces, October 2002, pp.423-428. Ferryman J, Shahrokni A. PETS2009: Dataset and challenge. In Proc. the 12th International Workshop on Per-formance Evaluation of Tracking and Surveillance (PETS-Winter), December 2009. Kuo C H, Huang C, Nevatia R. Multi-target tracking by on-line learned discriminative appearance models. In Proc. IEEE Conference on Computer Vision and Pattern Recog-nition, June 2010, pp.685-692. Zheng W S, Gong S, Xiang T. Reidentification by relative distance comparison. IEEE Trans. Pattern Analysis and Machine Intelligence, 2013, 35(3): 653-668. Yang B, Nevatia R. Online learned discriminative partbased appearance models for multi-human tracking. In Proc. the 12th European Conference on Computer Vision, October 2012, pp.484-498. Huang C, Wu B, Nevatia R. Robust object tracking by hierarchical association of detection responses. In Proc. the 10th European Conference on Computer Vision, October 2008, pp.788-801. Milan A, Schindler K, Roth S. Detection-and trajectorylevel exclusion in multiple object tracking. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2013, pp.3682-3689. Henriques J F, Caseiro R, Batista J. Globally optimal solution to multi-object tracking with merged measurements. In Proc. IEEE International Conference on Computer Vision, November 2011, pp.2470-2477. Zamir A R, Dehghan A, Shah M. GMCP-tracker: Global multi-object tracking using generalized minimum clique graphs. In Proc. the 12th European Conference on Com-puter Vision, October 2012, pp.343-356. Xu Y, Qin L, Li G, Huang Q. Online discriminative structured output SVM learning for multi-target tracking. IEEE Signal Processing Letters, 2014, 21(2): 190-194. Andriyenko A, Schindler K. Multi-target tracking by continuous energy minimization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.1265-1272. Wang B, Wang G, Chan K L, Wang L. Tracklet association with online target-specific metric learning. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2014, pp.1234-1241. Yang B, Nevatia R. Multi-target tracking by online learning a CRF model of appearance and motion patterns. Interna-tional Journal of Computer Vision, 2014, 107(2): 203-217. Pirsiavash H, Ramanan D, Fowlkes C C. Globally-optimal greedy algorithms for tracking a variable number of objects. In Proc. IEEE Conference on Computer Vision and Pat-tern Recognition, June 2011, pp.1201-1208. Berclaz J, Fleuret F, Turetken E, Fua P. Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Analysis and Machine Intelligence, 2011, 33(9): 1806-1819. Yu Q, Medioni G. Multiple-target tracking by spatiotemporal Monte Carlo Markov chain data association. IEEE Trans. Pattern Analysis and Machine Intelligence, 2009, 31(12): 2196-2210. Rasmussen C, Hager G D. Probabilistic data association methods for tracking complex visual objects. IEEE Trans. Pattern Analysis and Machine Intelligence, 2001, 23(6): 560-576. Guo C C, Chen S Z, Lai J H, Hu X J, Shi S C. Multi-shot person re-identification with automatic ambiguity inference and removal. In Proc. International Conference on Pattern Recognition, August 2014, pp.3540-3545. Johnson S C. Hierarchical clustering schemes. Psychome-trika, 1967, 32(3): 241-254. Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, 24(4): 509-522. Lian G, Lai J H, Zheng W S. Spatial-temporal consistent labeling of tracked pedestrians across non-overlapping camera views. Pattern Recognition, 2011, 44(5): 1121-1136. 372 J. Comput. Sci. & Technol., Mar. 2015, Vol.30, No.2 Cheung Y. Rival penalization controlled competitive learning for data clustering with unknown cluster number. In Proc. the 9th International Conference on Neural Informa-tion Processing, November 2002, pp.467-471. Dalal N, Triggs B. Histograms of oriented gradients for human detection. In Proc. IEEE Conference on Computer Vi-sion and Pattern Recognition, June 2005, pp.886-893. Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. In Proc. the 17th International Conference on Pattern Recognition, August 2004, pp.28-31. Hare S, Saffari A, Torr P. Struck: Structured output tracking with kernels. In Proc. IEEE International Conference on Computer Vision, November 2011, pp.263-270. Kalal Z, Mikolajczyk K, Matas J. Tracking-learningdetection. IEEE Trans. Pattern Analysis and Machine In-telligence, 2012, 34(7): 1409-1422.