SPRTracker: Learning Spatial-Temporal Pixel Aggregations for Multiple Object Tracking

Recently, multiple object tracking based on a single frame has achieved excellent performance. However, in crowded scenes, occlusion and motion blur will increase the difficulty of foreground object detection. In this letter, we propose a Spatial-temporal Pixel Resampling Tracker (SPRTracker) that introduces a novel cross-frame input framework to improve the anti-occlusion and anti-interference ability during tracking. We first propose a sampling mechanism in which Inter-frame Pixel Alignment (IPA) is designed to maintain spatial and temporal consistency in the propagation of pixel-level information between frames, aiming to improve the anti-interference ability during tracking target motion. Secondly, To improve the compensation effect of the gain information in the historical frame to the occluded target in the current frame, Similarity Reparameter Fusion (SRF) strategy is designed to fuse the features of the two frames. We evaluated the proposed method on two common benchmarks (MOT17 and MOT20), and the experimental results effectively demonstrated the superiority of our method.