Dynamic target tracking based on corner enhancement with Markov decision process

The Journal of Engineering

Guoyu Zuo¹  Lei Ma²  Tingting Du³ 

[1] Beijing Key Laboratory of Computing Intelligence and Intelligent System , Beijing , People'Faculty of Information Technology , Beijing University of Technology , Beijing , People's Republic of China

关键词: offline learning;    multiple MDP models;    traditional TLD algorithm;    multiple targets;    MDP strategy;    online;    MDP model;    tracking robustness;    target feature;    feature points;    single target tracking;    strong corners;    corner enhancement;    MDP target tracking method;    good tracking performance;    tracking process;    dynamic target tracking algorithm;    strategy problem;    tracking–learning–detection algorithm;    dynamic target tracking method;    tracking experiments;    Markov decision process model;    similarity function learning;    Shi-Tomasi corner method;    multitarget tracking problem;    reinforcement learning method;   

DOI  :  10.1049/joe.2018.8284

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

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.

【 授权许可】

CC BY