Single and Multiple View Detection, Tracking and Video Analysis in Crowded Environments

In this paper, we present our detection, tracking and event recognition methods and the results for PETS 2012. First, ROIs ( Regionsof Interest) based on geometric constraints are utilized in single view detection to eliminate the negative influence of clutter environment. Then, an optimized observation model is applied to address the ID switching or tracking drifting problem in single view tracking. Third, we introduce the multi-view Bayesian network (MBN) to reduce the "phantom" phenomena which frequently happen in general multi-view detection tasks. At last, a motion-based event recognition method is proposed to handle the event recognition task. Experimental results on the PETS 2012 dataset indicate that our methods are very promising.