Abstract: In recent years, behavior analysis in crowded environments has drawn increasing attentions in the computer vision community because of its wide industrial applications and important scientific values. In video surveillance, automatically detecting abnormal and dangerous behaviors plays an important role in ensuring public safety. In crowd control, recognizing traffic patterns and estimating traffic flows provide valuable information for avoiding congestion. The long-term statistical information from crowd behavior analysis provides guidelines for planning and designing crowded public areas in order to increase their safety and to optimize their traffic capacity. The collective behaviors of crowds show striking analogies with some self-organization phenomena observed in other social processes and other fields such as physics and biology. Therefore, automatic crowd behavior analysis also has scientific values in interdisciplinary fields.

This talk will introduce two types of models, hierarchical Bayesian models and agent-based models, based on moving pixels or highly fragmented trajectories to solve this challenge. Hierarchical Bayesian models jointly model simple activities of individual, interactions between individuals, and complex behaviours of the whole scene at different levels. Agent-based models model the process of individuals making decisions of their actions based on the current states. Results on a traffic scene and a train station scene will be shown.

Bio
Xiaogang Wang received his Bachelor degree in Electrical Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China, M.Phil. degree in Information Engineering from the Chinese University of Hong Kong, and PhD degree in Computer Science from Massachusetts Institute of Technology. He is an assistant professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He was the Area Chair of IEEE International Conference on Computer Vision (ICCV) 2011. He received the Outstanding Young Researcher in Automatic Human Behaviour Analysis award in 2011. His research interests include computer vision, machine learning and medical vision.  He has published more than 50 papers on top conferences and journals. His work has been cited for 1500 times in Google Scholar.