Multiple pedestrian tracking based on couple-states Markov chain with semantic topic learning for video surveillance

Robust, accurate and efficient pedestrian tracking is a critical task in intelligent visual surveillance systems and robotic vision applications. Unfortunately, tracking in realistic scenarios is not easy and may fail due to the challenges of partial occlusion, viewpoint changes and cluttered background. To overcome these challenges, different tracking strategies have been proposed to model the tracking process as a first-order temporal Markov chain. It has been well-known that target appearance modeling approaches play essential function in this process, but most these approaches only represent the object characteristics in the pixel/texture level instead of investigating the latent information in the semantic understanding level. Therefore, the obtained optimum state based on the texture similarity may not be as same as observed by human vision system. To resolve this limitation, in this paper, we proposed a multiple pedestrian tracking algorithm based on couple-states analysis, the hidden state is used to obtain the estimated observations during the Markov chain transition process, and the latent state is used to find the semantic information about each observation. By maximizing the likelihood probability of the couple states for each estimation, the optimum state of target can be found more accurately, and error accumulation can also be effectively decreased during tracking. The performance of the proposed tracking has been verified on different benchmark surveillance video database, the results showed that the proposed tracking is able to track multiple pedestrians more accurately in variety of challenge scenarios when compared with other state-of-art multiple pedestrian tracking approaches.