摘要
The region completeness of object detection is very crucial to video surveillance, such as the pedestrian and vehicle identifications. However, many conventional object detection approaches cannot guarantee the object region completeness because the object detection can be influenced by the illumination variations and clustering backgrounds. In order to overcome this problem, we propose the iterative superpixels grouping （ISPG） method to extract the precise object boundary and generate the object region with high completeness after the object detection. First, by extending the superpixel segmentation method, the proposed ISPG method can improve the inaccurate segmentation problem and guarantee the region completeness on the object regions. Second, the multi- resolution superpixel-based region completeness enhancement method is proposed to extract the object region with high precision and completeness. The simulation results show that the proposed method outperforms the conventional object detection methods in terms of object completeness evaluation.
The region completeness of object detection is very crucial to video surveillance, such as the pedestrian and vehicle identifications. However, many conventional object detection approaches cannot guarantee the object region completeness because the object detection can be influenced by the illumination variations and clustering backgrounds. In order to overcome this problem, we propose the iterative superpixels grouping （ISPG） method to extract the precise object boundary and generate the object region with high completeness after the object detection. First, by extending the superpixel segmentation method, the proposed ISPG method can improve the inaccurate segmentation problem and guarantee the region completeness on the object regions. Second, the multi- resolution superpixel-based region completeness enhancement method is proposed to extract the object region with high precision and completeness. The simulation results show that the proposed method outperforms the conventional object detection methods in terms of object completeness evaluation.
基金
supported in part by the“MOST”under Grant No.103-2221-E-216-012
