Journal of Shanghai University >
Segmenting groups in crowd based on spatiotemporal restraint
Received date: 2015-11-02
Online published: 2017-10-30
Pedestrians in crowded scenes can be seen as a collection of groups moving coherently. To segment the crowd into groups and analyze interactions between them, a method using group spatiotemporal relationships is proposed. Spatiotemporal information of crowds by modeling the background and tracking the feature points is acquired. Then the individuals are grouped according to aspatial neighborhood restraint. The groups are further divided by motion correlation over time. These two restraints work with each other effectively, and generate groups with coherent motion. Tests on many videos of real-world pedestrian scenes show that the method can be applied to a variety of scenes with different crowd densities and perspective of videos.
Key words： coherent motion; spatiotemporal restraint; sub-group; crowd segmentation
YI Xian1,2, DONG Nan2, WEI Jianming2, ZHU Wenhao1 . Segmenting groups in crowd based on spatiotemporal restraint[J]. Journal of Shanghai University, 2017 , 23(5) : 742 -751 . DOI: 10.12066/j.issn.1007-2861.1760
[1] Ben X Y, Huang X F, Zhuang Z Y, et al. Agent-based approach for crowded pedestrian evacuation simulation [J]. Intelligent Transport Systems, 2013, 7(1): 55-67.
[2] Li J Y, Lu X F, Ding L Y, et al. Moving target tracking via particle filter based on color and contour features [C]// Proceedings of International Conference on Information Engineering and
Computer Science. 2010: 1-4.
[3] Zhang Q, Rui T, Fang H S, et al. Particle filter object tracking based on Harris-SIFT feature matching [J]. Procedia Engineering, 2012, 29: 924-929.
[4] Andrade E L, Blunsden S, Fisher R B. Modeling crowd scenes for event detection [C]//Proceedings of 18th International Conference on Pattern Recognition. 2006: 175-178.
[5] Andrade E L, Fisher R B. Simulation of crowd problems for computer vision [C]// First International Workshop on Crowd Simulation. 2005: 71-80.
[6] Chan A, Vasconcelos N. Modeling, clustering, and segmenting video with mixtures of dynamic textures [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(5): 909-926.
[7] Aveni A. The not-so-lonely crowd: friendship groups in collective behavior [J]. Sociometry, 1977, 40: 96-99.
[8] Karamouzas I, Overmars M. Simulating and evaluating the local behavior of small pedestrian groups [J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(3): 394-406.
[9] Lan T, Wang Y, Yang W L, et al. Discriminative latent models for recognizing contextual group activities [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,
34(8): 1549-1562.
[10] Moussaid M, Perozo N, Garnier S, et al. The walking behavior of pedestrian social groups and its impact on crowd dynamics [J]. PloS One, 2010, 5(4): e10047.
[11] Chang M C, Krahnstoever N, Lim S, et al. Group level activity recognition in crowded environments across multiple cameras advanced video and signal based surveillance [C]// Proceedings of International Conference on Advanced Video and Signal Based Surveillance. 2010: 56-63.
[12] Ge W N, Collins R T, Ruback B. Automatically detecting the small group structure of a crowd [C]// Proceedings of Workshop on Applications on Computer Vision. 2009: 1-8.
[13] Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for realtime tracking with shadow detection [C]// Proceedings of 2nd European Workshop on Advanced Video Based Surveillance Systems. 2001: 149-158.
[14] Tomasi C, Kanade T. Detection and tracking of point features [J]. International Journal of Computer Vision, 1991, 9: 137-154.
[15] Shao J. CUHK crowd dataset [EB/OL]. [2015-04-30]. http://www.ee.cuhk.hk/jshao/cuHKcrowdfiles/cuhk-dataset-htm.
[16] Shao J, Chen C L, Wang X G. Scene-independent group profiling in crowd [C]// Proceedings of Conference on Computer Vision and Pattern Recognition. 2014: 2227-2234.
[17] Zhou B, Tang X, Wang X. Coherent filtering: detecting coherent motions from crowd clutters [C]// Proceedings of Conference on Computer Vision. 2012: 857-871.
[18] Barnich O, Van Droogenbroeck M. Vibe: a universal background subtraction algorithm for video sequences [J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.
/
| 〈 |
|
〉 |
