Crowd analysis studies the crowd phenomenon and its dynamics. The steady population growth, along with the worldwide urbanization, has made the crowd phenomenon more frequent. Crowd analysis has received attention from technical and social research disciplines. The crowd phenomenon is of great interest in a large number of applications such as crowd management, crowd control, crowd behavior simulation, prediction and so on.
We focus on measuring crowd collectiveness, detecting coherent motions, finding semantic regions and activity perception.
Measuring Crowd Collectiveness
B. Zhou, X. Tang and X. Wang. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (Oral, CVPR 2013)
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models
X. Wang, X. Ma, and E. Grimson. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, pp. 539-555, 2009.
Unsupervised Activity Perception by Hierarchical Bayesian Models
X. Wang, X. Ma, and E. Grimson. in Proceedings of IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR) 2007.
Salient Motion Detection in Crowded Scenes
C. C. Loy, T. Xiang, and S. Gong, in Proceedings of International Symposium on Communications, Control and Signal Processing, pp. 1-4, 2012.
Surveillance Video Behaviour Profiling and Anomaly Detection
C. C. Loy, T. Xiang, and S. Gong, in Proceedings of Society of Photo-Optical Instrumentation Engineers Conference Series, 2009
From Local Temporal Correlation to Global Anomaly Detection
C. C. Loy, T. Xiang, and S. Gong, in Proceedings of European Conference on Computer Vision, International Workshop on Machine Learning for Vision-based Motion Analysis, 2008 (MLVMA @ ECCV).
Cumulative Attribute Space for Age and Crowd Density Estimation
K. Chen, S. Gong, T. Xiang, and C. C. Loy, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013 (CVPR, Oral).