关键词: 计算机视觉, 行人检测, 代价敏感, 粒子群, T变异

Abstract: A pedestrian detection algorithm based on mechanism of dynamic cost-sensitive parameters optimization is proposed. This algorithm introduces a cost-sensitive SVM classification algorithm by setting cost-sensitive parameters to process the problem of class-imbalanced between the sample size of pedestrian and non-pedestrian in an image. Taking into account the selection of cost-sensitive parameters’ values has a great influence on the detection performance, the paper proposes a chaotic particle swarm optimization algorithm based on T mutation that can improve the global search ability of particles by chaos algorithm and T mutation function, and using the best compromise between positive and negative samples correctly classified as the principle of optimization to optimize dynamically in the range of cost-sensitive weight value. Experimental results show that the pedestrian detection algorithm based on cost-sensitive parameters dynamic optimization mechanism is conducive to improve the detection accuracy.

Key words: computer vision, pedestrian detection, cost-sensitive, Particle Swarm Optimization（PSO）, T mutation