Research on Multi-Target Tracking Technology Based on Particle Filter

Abstract

Using CNN deep convolutional neural network model to extract image sample features, this paper designs an image multi-target tracking algorithm combining particle filter and Softmax classifier. The possible transformation state factors of the tracking target are considered in the design of the particle filter, the possible transformation states are defined by the Bayesian probability operation method, the particle filter is introduced into the CNN model, and the extracted particle image block is analyzed by the Softmax classifier. Features to achieve accurate classification. The single-target and multi-target tracking accuracy of the proposed algorithm is evaluated through four experiments. The experimental results verify that the premise of improving the accuracy of image multi-target tracking is to obtain accurate feature information of the tracked image and achieve accurate classification. The accuracy of single-target and multi-target tracking has reached more than 94%.