Abstract
Target tracking in video is a hot topic in computer vision field, which has wide applications in surveillance, robot navigation and human-machine interaction etc. Meanshift is widely used algorithm in video target tracking field. The basic mean shift algorithm only considers the color of targets as the tracking characteris- tic feature, so if the appearance of the target changes greatly or there exits other objects whose color is similar to the target, the tracking process will fail. To enhance the stability and robustness of the algorithm, we introduce par- ticle filter into the tracking process. Basic particle filter has some disadvantages such as low accuracy, high computational complexity. In this paper, an improved particle filter GA-UPF was proposed, in which a new re-sampling algorithm was used to predict target centroid position. The target tracking system of binocular stereo vision is designed and implemented. Experi- mental results have shown that our algorithm can tracking object in video with high accuracy and low computational complexity.
Highlights
Target tracking in video is a hot topic in computer vision field, which has wide applications in surveillance, robot navigation and human-machine interaction etc
GA-UPF is the integration of genetic algorithm (GA) and unscented particle filter (UPF)
All results are the means of 100 runs, as the results of UPF and GA-UPF are close and far different with the other algorithms
Summary
Target tracking in video is a hot topic in computer vision field, which has wide applications in surveillance, robot navigation and human-machine interaction etc. But if the appearance of the target changes greatly or there exits other objects who are affected by the occasion. To enhance the stability and robustness of the algorithm, particle filter is introduced into the tracking process. Due to solve the problem, an improved particle filter GA-UPF was proposed. GA-UPF is the integration of genetic algorithm (GA) and unscented particle filter (UPF). It successfully offsets the defect in PF. The remaining of the paper is organized as follows: in the Section 2, a brief description of GPF is presented. The details of the new PF this paper proposed – GA-UPF is presented.
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