关键词: 目标跟踪, 有效块粒子, 蒙特卡罗方法, 多特征融合

Abstract: Moving target tracking is a hotspot in the field of computer vision. For the current moving target method, the overall information in the target frame will be considered when tracking the target, but the shape of the tracked target is variability, resulting in low robustness and accuracy of the tracking method. In this paper, using the local structure of the tracked target, the tracking target is regarded as a block particle. Under the sequence Monte Carlo method, the block particle confidence function and the motion similarity function are constructed, and the best position of the tracking target is calculated by weight voting. Based on the problem of insufficient single feature extraction based on basic tracking method, a method based on multi-feature linear fusion of HOG and color histogram is proposed. Experiments show that compared with a variety of excellent tracking methods, the proposed method achieves the lowest average tracking error of 14.83 and the target overlap rate of 0.67, which indicates that the algorithm can perform target tracking stably and accurately.

Key words: target tracking, effective blockparticle, Monte Carlo method, multi-feature fusion