Particle Filtering Based Visual Attention Model for Moving Target Detection

visual attention is important research in the field of compute vision, which is widely used in target detection and target tracking. In this paper, a method of moving object detection based on visual attention model and particle filter is proposed. Firstly, the particle weight calculation process is established by using Bayesian theory and visual bidirectional (top-down/bottom-up)fusion method; then, note that the model uses the target motion attention and the target color attention as inputs, and uses the importance sampling, particle weight calculation, resampling, and particle saliency map processing to calculate the saliency of the moving target; Finally, the distribution of particles determines the position of the target. Through testing in different scenes and videos, it is concluded that this method is more accurate and efficient than the traditional method for moving target detection.