Robust tracking of multiple persons in real-time video

In this paper, we present a robust person tracking method that the particle swarm optimization (PSO) algorithm is used as the tracking strategy. The method is divided into two procedures: object/background segmentation and tracking. For object/background segmentation, we use the temporal differencing to detect the regions of interest. For tracking, the PSO algorithm is used for overcome the robustness problem in the high noisy background and multiple moving persons and/or under occlusion. The particles in PSO represent the position, width and height of the search window, and the fitness function is calculated by the distance of the color feature vector and the histogram intersection. When occluded, we add the motion vector plus the previous position of the tracking model. The particles fly around the search region to obtain an optimal match of the target. The experiments show that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the conventional particle filter method.