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Crowd density analysis is a crucial component in visual surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density measure, in which local information at pixel level substitutes a global crowd level or a number of people per-frame. The proposed approach consists of generating fully automatic and crowd density maps using local features as an observation of a probabilistic crowd function. It also involves a feature tracking step which allows excluding feature points belonging to the background. This process is favorable for the later density function estimation since the influence of features irrelevant to the underlying crowd density is removed. Our proposed approach is evaluated on videos from different datasets, and the results demonstrate the effectiveness of feature tracks for crowd estimation. Furthermore, we include a comparative study between different local features in order to investigate their discriminative power to the crowd.

The study of crowd behavior in public areas or during some public events is receiving a lot of attention in security community to detect potential risk and to prevent overcrowd. In this paper, we propose a novel approach for change detection, event recognition and characterization in human crowds. It consists of modeling time-varying dynamics of the crowd using local features. It also involves a feature tracking step which allows excluding feature points on the background and extracting long-term trajectories. This process is favourable for the later crowd event detection and recognition since the influence of features irrelevant to the underlying crowd is removed and the tracked features undergo an implicit temporal filtering. These feature tracks are further employed to extract regular motion patterns such as speed and flow direction. In addition, they are used as an observation of a probabilistic crowd function to generate fully automatic crowd density maps. Finally, the variation of these attributes (local density, speed, and flow direction) in time is employed to determine the ongoing crowd behaviors. The experimental results on two different datasets demonstrate the effectiveness of our proposed approach for early detection of crowd change and accurate results for event recognition and characterization.