AN ONLINE CROWD SEMANTIC SEGMENTATION METHOD BASED ON REINFORCEMENT LEARNING

In this paper, we propose an online crowd segmentation method based on reinforcement learning. Existing approaches for the task suffers from the fixed parameters applied to different sceneries. We propose to utilize the reinforcement learning to adaptively adjust the critical parameters for better clustering. Specifically, we first propose the velocity-constrained natural nearest neighbor algorithm to adaptively determine the original fixed parameter K in K -nearest-neighbor, thus better reflecting the neighborhood relationship in velocity correlation calculation. Then, we construct an online reinforcement learning module in the grouping process to adaptively decide the segmentation threshold. Furthermore, we combine the motion image semantics with feature point grouping to obtain the pixel-level segmentation. This method enhances the generalization and robustness in different scenarios to increase the accuracy. Comprehensive experimental results conducted on different datasets demonstrate the effectiveness and validity of our method.