Dynamic target tracking based on corner enhancement with Markov decision process

The tracking–learning–detection (TLD) algorithm applied in the home environment can effectively improve the tracking robustness. However, it has the problems of single target tracking and poor selection of feature points. This study proposed a dynamic target tracking method based on corner enhancement with Markov decision process (MDP) model. The MDP target tracking method is adopted to change a multi‐target tracking problem into a strategy problem based on MDP model, in which one MDP model represents the life cycle of a target, and multiple targets are represented by multiple MDP models. In the tracking process, the strong corners generated by the Shi‐Tomasi corner method are used to replace the feature points generated by the traditional TLD algorithm at intermediate intervals, which makes the target feature points more stable during the tracking process. The similarity function learning for data association is equivalent to the learning of the MDP strategy, in which the reinforcement learning method is used and has double advantages of both online and offline learning. The tracking experiments with different data sets are performed, and the results show that dynamic target tracking algorithm based on the corner enhancement with MDP has both good tracking performance and good anti‐interference capability.