Robust Multi-Object Tracking With Spatial Uncertainty

Shang-Hong Lai, Pin-Jie Liao, Yu Cheng Huang, Chen-Kuo Chiang

Abstract: Most methods address the multi-object tracking (MOT) problem by tracking-by-detection paradigm, which tracks objects from the detected windows by associating detection boxes whose scores are higher than a given threshold. As such, the confidence score becomes the only indicator of bounding boxes when handling complicated cases, such as occlusions. However, a high confidence score cannot guarantee that the bounding box does not overlap with nearby objects, especially in crowded scenarios. In this paper, spatial uncertainty is proposed for MOT. Firstly, the statistical analysis indicates that spatial uncertainty is highly correlated to the occlusion ratio, which can better represent the level of occlusion of the detection boxes. It is measured by the proposed Sparse Tracker with Spatial Uncertainty (SSUTracker). Then, it is adopted to learn robust tracklet representation. The experimental results demonstrate that it improves overall performance. As a result, our approach achieves very competitive results on popular MOT17 and MOT20 benchmarks compared to state-of-the-art methods.