[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2019.02.023

Collective Interaction Filtering Approach for Detection of Group in Diverse Crowded Scenes

Wong, Pei Voon (Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman)
Mustapha, Norwati (Faculty of Computer Science and Information Technology, University Putra Malaysia)
Affendey, Lilly Suriani (Faculty of Computer Science and Information Technology, University Putra Malaysia)
Khalid, Fatimah (Faculty of Computer Science and Information Technology, University Putra Malaysia)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.2, 2019 , pp. 912-928 More about this Journal

Abstract

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

Keywords

group detection; clustering; crowded scenes; trajectory; behavior analysis;

Citations & Related Records

Reference

1	X. Wang and C. Loy, "Deep Learning for Scene-Independent Crowd Analysis," in Group and Crowd Behavior for Computer Vision, 1st ed., Elsevier, pp. 209-252, 2017.
2	M. Rodriguez, J. Sivic, I. Laptev, and J.-Y. Audibert, "Data-driven crowd analysis in videos," in Proc. of 2011 International Conference on Computer Vision, pp. 1235-1242, 2011.
3	M. Jiang, J. Huang, X. Wang, J. Tang, and C. Wu, "An Approach for Crowd Density and Crowd Size Estimation," J. Softw., vol. 9, no. 3, pp. 757-762, Mar. 2014.
4	F. Nielsen, "K-MLE: A fast algorithm for learning statistical mixture models," in Proc. of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 869-872, June 2012.
5	J. C. S. J. Junior, S. R. Musse, and C. R. Jung, "Crowd Analysis Using Computer Vision Techniques," IEEE Signal Process. Mag., vol. 27, no. 5, pp. 66-77, Sep. 2010. DOI
6	M. Thida, Y. L. Yong, P. Climent-perez, H. Eng, and P. Remagnino, "A Literature Review on Video Analytics of Crowded Scenes," in Intelligent Multimedia Surveillance, Springer Berlin Heidelberg, pp. 17-36, 2013.
7	T. Li, H. Chang, M. Wang, B. Ni, R. Hong, and S. Yan, "Crowded Scene Analysis: A Survey," IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 3, pp. 367-386, Mar. 2015. DOI
8	Z. Li and J. Tang, "Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval," IEEE Trans. Multimed., vol. 17, no. 11, pp. 1989-1999, Nov. 2015. DOI
9	B. Zhou, X. Wang, and X. Tang, "Random field topic model for semantic region analysis in crowded scenes from tracklets," Cvpr 2011, pp. 3441-3448, 2011.
10	B. Zhou, X. Tang, H. Zhang, and X. Wang, "Measuring crowd collectiveness," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 8, pp. 1586-1599, 2014. DOI
11	R. Liang, Y. Zhu, and H. Wang, "Counting crowd flow based on feature points," Neurocomputing, vol. 133, pp. 377-384, 2014. DOI
12	A. M. Cheriyadat and R. J. Radke, "Detecting dominant motions in dense crowds," IEEE J. Sel. Top. Signal Process., vol. 2, no. 4, pp. 568-581, 2008. DOI
13	J. Trojanova, K. Krehnac, and F. Bremond, "Data-Driven Motion Pattern Segmentation in a Crowded Environments," Florida dental journal, vol. 9913, no. 4, G. Hua and H. Jegou, Eds. Cham: Springer International Publishing, pp. 760-774, 2016.
14	V. J. Kok, M. K. Lim, and C. S. Chan, "Crowd behavior analysis: A review where physics meets biology," Neurocomputing, vol. 177, pp. 342-362, Feb. 2016. DOI
15	B. Zhou, X. Tang, and X. Wang, "Coherent filtering: Detecting coherent motions from crowd clutters," LNCS, vol. 7573 LNCS, no. PART 2, pp. 857-871, 2012.
16	W. Hu, T. Tan, L. Wang, and S. Maybank, "A Survey on Visual Surveillance of Object Motion and Behaviors," IEEE Trans. Syst. Man Cybern. Part C (Applications Rev., vol. 34, no. 3, pp. 334-352, 2004.
17	W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank, "A system for learning statistical motion patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 9, pp. 1450-1464, 2006. DOI
18	R. Li and R. Chellappa, "Group motion segmentation using a Spatio-Temporal Driving Force Model," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2038-2045, 2010.
19	W. Ge, R. T. Collins, and R. B. Ruback, "Vision-Based Analysis of Small Groups in Pedestrian Crowds," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 5, pp. 1003-1016, May 2012. DOI
20	S. Cosar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares, and F. Bremond, "Toward Abnormal Trajectory and Event Detection in Video Surveillance," IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 3, pp. 683-695, Mar. 2017. DOI
21	C. Tomasi, "Detection and Tracking of Point Features Technical Report CMU-CS-91-132," Image Rochester NY, vol. 91, no. April, pp. 1-22, 1991.
22	S. Pellegrini, A. Ess, and L. Van Gool, "Improving Data Association by Joint Modeling of Pedestrian Trajectories and Groupings," LNCS, vol. 6311 LNCS, no. PART 1, pp. 452-465, 2010.
23	B. Zhou, X. Wang, and X. Tang, "Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents," in Proc. of IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2871-2878, 2012.
24	C. Zhang, H. Li, X. Wang, and X. Yang, "Cross-scene crowd counting via deep convolutional neural networks," in Proc. of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 833-841, 7-12 June, 2015.
25	L. Lu, C.-Y. Chan, J. Wang, and W. Wang, "A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model," Transp. Res. Part C Emerg. Technol., vol. 81, pp. 317-329, Aug. 2017. DOI
26	F. Solera, S. Calderara, E. Ristani, C. Tomasi, and R. Cucchiara, "Tracking Social Groups Within and Across Cameras," IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 3, pp. 441-453, Mar. 2017. DOI
27	I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, "Support vector machine learning for interdependent and structured output spaces," in Proc. of Twenty-first international conference on Machine learning - ICML '04, p. 104, 2014.
28	J. Shao, K. Kang, C. C. Loy, and X. Wang, "Deeply learned attributes for crowded scene understanding," in Proc. of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4657-4666, 2015.
29	J. Shao, C. C. Loy, K. Kang, and X. Wang, "Slicing Convolutional Neural Network for Crowd Video Understanding," in Proc. of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5620-5628, 2016.
30	P. V. Wong, N. Mustapha, L. S. Affendey, and F. Khalid, "A new clustering approach for group detection in scene-independent dense crowds," in Proc. of 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), pp. 414-417, 2016.
31	A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. R. Stat. Soc. Ser. B Methodol., vol. 39, no. 1, pp. 1-38, 1977.
32	S. Ali and M. Shah, "A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis," in Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-6, 2007.
33	J. Shao, C. C. Loy, and X. Wang, "Learning Scene-Independent Group Descriptors for Crowd Understanding," IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 6, pp. 1290-1303, Jun. 2017. DOI
34	F. Solera, S. Calderara, and R. Cucchiara, "Socially Constrained Structural Learning for Groups Detection in Crowd," IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 5, pp. 995-1008, 2016. DOI
35	V. Murino, M. Cristani, S. Shah, and S. Savarese, Chapter 1 - The Group and Crowd Analysis Interdisciplinary Challenge, 1st ed. Elsevier Inc., 2017.