Human Detection with Occlusion Handling by Over-Segmentation and Clustering on Foreground Regions

Two-dimensional image based human detection methods have been widely used in surveillance system. However, detecting human in the presence of occlusion is still a challenge for such image based systems. In this paper, a human detection method aiming to handle occlusions by using the depth data obtained from 3D imaging methods, such as those easily acquired from the Microsoft Kinect depth sensor, is proposed. In the context of surveillance setting, background subtraction on the depth data can be used to extract foreground regions which may correspond to humans. The proposed method analyzes the 3D data of the foreground regions using a "split-merge" approach. Over-segmentation and clustering are preformed on foreground regions followed by the height validation. Experimental results demonstrate that the proposed method outperforms two state-of-art human detection methods.