A pedestrian detection method based on PSO and multimodal function

To solve the problem of heavy time-consuming in real-time pedestrian detection, a pedestrian detection algorithm based on Particle Swarm Optimization (PSO) for multimodal function is developed. This method treats the pedestrian detection problem as the multimodal function in a solution space, and uses PSO to solve the multimodal function. Pedestrians' feature is described by Histogram of Sparse Code (HSC) and classified by linear SVM. In addition, the acceleration constants in PSO are improved by adaptive parameters, and the proposed iteration criterion is given to find the final results efficiently and effectively. Simulation experiments show the effectiveness of the proposed algorithm, and the running time is decreased by 72.9% compared to the sliding window detection methods.