LAUSR

Multiple object tracking has improved drastically in recent years due to one-shot tracking methods. These methods design joint-detection-and-tracking structures to achieve real-time tracking performance and introduce more powerful detectors to deal with missed objects. However, most of them perform poorly in crowded scenes because of frequent occlusions. Several previous works have attempted to alleviate the occlusion issue, but they hardly involve the essence of the problem. In this letter, we suppose that occlusion is closely related to crowd density, so the degree of occlusion can be estimated. Therefore, we propose a Potential Object Mining strategy to adaptively obtain occluded objects for reducing broken trajectories, which re-weights detections based on the predicted density map. Additionally, for the strategy, Dense Estimator is designed to predict the density of each region in an image by employing a Transformer-based structure. Combining them together forms our Adaptive Occlusion Handling (AOH) tracking framework. Extensive experiments on MOTChallenge benchmarks (MOT17 and MOT20) demonstrate that our AOH achieves the state-of-the-art performance, especially on the heavily occluded MOT20.