Research on Multi-target Tracking Technology Based on Deep Reinforcement Learning
In the process of multi-target tracking in random finite sets,due to the complexity of the tracking problem,it can consume a lot of calculation costs.Especially in complex situations where targets and clutters are dense,the calculation cost increases exponentially.The time complexity of the assignment algorithm commonly used in random finite sets,such as the Murty algorithm,is the cubic of the size of the cost matrix generated by the filter.To reduce tracking time,this paper integrates the concept of combinatorial optimization,reformulates the cost matrix as a bipartite graph,and adopts an online bipartite graph matching algorithm based on deep reinforcement learning to replace the traditional allocation algorithm in random finite sets.The feasibility of the method is confirmed through simulation experiments.Experiments demonstrate that this method reduces tracking time while maintaining tracking performance and enhancing the real-time efficiency of tracking.
random finite setsreinforcement learningcombinatorial optimization
万方数据
维普
