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Moving Object Detection for Video Based on Image Blocking and Improved Particle Filter

In this paper, a moving object detection method based on image blocking and improved Particle Filter is proposed. Firstly, each frame in a video is partitioned into blocks and a low-dimensional descriptor is extracted for each block; Secondly, each block is sequentially passed through two classifiers, improved Particle Filter and temporal correlation check. Finally, foreground object is detected by integrating the classification results of all blocks. We use two-stage classifier to effectively minimize the number of false positive in the detected foreground object. Experiments show that the method proposed in this paper is feasible both in qualitative and quantitative analysis so it can be used in moving object detection effectively.