Object detection via superpixel and 3D self-organizing background subtraction

For moving targets with slow speed and temporary stationary, the detection performance of traditional methods via Gauss model and three-frame model is not so satisfactory. Therefore, a novel scheme is proposed to improve the detection performance. Specifically, the simple linear iterative clustering algorithm method is first utilized to complete the superpixel segmentation; then, the 3D self-organizing background subtraction algorithm is utilized to achieve the background model; finally, the optimal weight decision strategy is utilized to detect moving targets. Experimental results conducted on MSA and PETS2009 datasets demonstrate that the proposed scheme can effectively improve the object detection performance.