Hierarchical Crowd Detection and Representation for Big Data Analytics in Visual Surveillance

In this paper, a motion and appearance saliency combined detection framework for hierarchical representation of targets from groups to individuals in crowded scenes of surveillance videos is proposed. Big data analytic solutions within surveillance often require compact representations for target (s)-of-interest that allows simultaneous micro (individualistic) and macro (holistic) levels of inference on visual information. The target detection method proposed in this paper combines the estimation of motion saliency through dynamic texture (DT) based Gaussian Mixture Model (GMM) and appearance saliency through person detection using combined Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) feature sets. The saliency models are tightly integrated such that initially motion information is used to update and improve detection within an appearance framework, which in turn compliments the motion segmentation for accurate localization of people in groups. The improved people detection thus proposed is capable of eliminating false detections and can accurately delineate individuals within groups. The quantitative and qualitative results of experiments conducted on benchmark datasets have proven the validity and robustness of the proposed technique.