Robust real-time attention-based head-shoulder detection for video surveillance

Robust head-shoulder detection has widely been utilized in video surveillance applications. However, most state-of-the-art approaches are very time-consuming and unable to effectively handle videos with radical pose and viewpoint changes. In this paper, we introduce a robust and rapid head-shoulder detection method, which is invariant to pose and viewpoint for video surveillance. The proposed method combines an attention-based foreground segmentation module and a multiview head-shoulder detection cascade to achieve high performance in both accuracy and speed. Specifically, the attention-based foreground segmentation module firstly detects not only active regions that involve motions but also static areas where people stand or sit still in video frames. These regions are then provided to a two-layer head-shoulder detection cascade which is composed of a preliminary linear classifier which eliminates most obvious non-head-shoulder windows rapidly, and a set of histogram intersection kernel (HIK) based multi-view classifiers that can precisely detect head-shoulders with different pose and viewpoint. When compared to the leading methods in the challenging PETS 2009 benchmark dataset, our approach obtains highly competitive results in terms of effectiveness and efficiency.