Detection and handling of occlusion in an object detection system

Object detection is an important technique for video surveillance applications. Although different detection algorithms were proposed, they all have problems in detecting occluded objects. In this paper, we propose a novel system for occlusion handling and integrate this in a sliding-window detection framework using HOG features and linear classification. The occlusion handling is obtained by applying multiple classifiers, each covering a different level of occlusion and focusing on the non-occluded object parts. Experiments show that our approach based on 17 classifiers, obtains an increase of 8% in detection performance. To limit computational complexity, we propose a cascaded implementation that only increases the computational cost by 3.4%. Although the paper presents results for pedestrian detection, our approach is not limited to this object class. Finally, our system does not need an additional dataset for training, covering all possible types of occlusions.