New insights into Crowd Density Analysis in Video Surveillance Systems

Crowd analysis has recently emerged as an increasingly important problem for crowd monitoring and management in the visual surveillance community. In this thesis, our objectives are to address the problems of crowd density estimation and to investigate the usefulness of such estimation as additional information to other applications. Towards the first goal, we focus on the problems related to the estimation of the crowd density using low level features in order to avert typical problems in detection of high density crowd. We demonstrate in this dissertation, that the proposed approaches perform better than the baseline methods, either for counting people, or alternatively for estimating the crowd level. Afterwards, we propose a novel approach, in which local information at the pixel level substitutes the overall crowd level or person count. It is based on modeling time-varying dynamics of the crowd density using sparse feature tracks as observations of a probabilistic density function. The second goal is to use crowd density as additional information to complement other tasks related to video surveillance in crowds. First, we use the proposed crowd density measure which conveys rich information about the local distributions of persons to improve human detection and tracking in videos of high density crowds. Second, we investigate the concept of crowd context-aware privacy protection by adjusting the obfuscation level according to the crowd density. Finally, we employ additional information about the local density together with regular motion patterns as crowd attributes for high level applications such as crowd change detection and event recognition.