Aiming at the multi-objective pedestrian trajectory tracking problem in public space, a multi-objective pedestrian trajectory tracking algorithm based on reinforcement learning is proposed. Firstly, a high-precision target detector is utilized to detect pedestrian targets in space, and an independent single-target tracker is assigned for each tracking target trajectory. Each target is trained as an agent by means of deep reinforcement learning, and combined with the appearance and position characteristics between tracking trajectory and detecting target, the tracking target is constructed. Similarity cost matrix is built to realize data association through Hungarian algorithm. Experiments show that the tracking accuracy of this algorithm is 76.1% on common open data sets. Good results have been achieved in multi-objective pedestrian trajectory tracking in public space.