Research Of Moving Object Detection In Video Sequences

Moving object detection is an important branch and foundation of computer vision; it has extensive application prospects in many fields, such as, military applications, traffic, industries and bio-medical, et al, and attracts broad attention, and becomes a hot research topic in computer vision field. Because of its inherent complexity, moving object detection still faces lots of challenges. In the paper, based on the existing research achievements, the methods of moving object detection in static and dynamic scene are studied deeply.For moving object detection in static scene, the paper proposes a modified codebook algorithm, and performs validation experiment with several indoor and outdoor video sequences. The results prove that the proposed algorithm can meet with the application requirements. The original work in this section mainly includes the following points:(1) Based on a new color model, a new high-light and shadow judge rule is proposed.(2) A new codebook abstracting, real-time codebook updating, and self-study policy is used.For moving object detection in dynamic scene, the paper introduces two algorithms to perform motion estimation and compensation. Firstly, a global motion estimation algorithm based on block matching is proposed, this algorithm combines several ideas, such as feature selection templet, Canny edge detection operator, new triangle-diamond search policy, and motion-vector abstraction, et al, to reduce the operation complexity, and insure and improve the precision of motion estimation. However, this algorithm uses translation transform model, it is only suitable for video sequences whose global motion style is panning. Then, a global motion estimation algorithm based on feature matching is proposed. In order to estimate the parameter of global motion model well and truly, it combines several ideas, such as feature selection templet, SIFT operator, least squared criterion policy, revision of feature points matching-pairs, and bilinear interpolation, et al. Since this algorithm uses affine transform model, it has universality, and can suitable for video sequences whose global motion style including panning, tilting, zooming, et al. The two algorithms are all validated with one or several standard indoor and outdoor video sequences. The results demonstrate that the two algorithms have good robusticity and validity in respective application fields. The original work in this section mainly includes the following points:(1) A threshold policy is proposed to abstract the global motion vectors.(2) A new triangle-diamond search policy is proposed.(3) A new revising policy of feature point matching-pairs is proposed.