DET: Depth-Enhanced Tracker to Mitigate Severe Occlusion and Homogeneous Appearance Problems for Indoor Multiple-Object Tracking

Multiple-object tracking has long been a topic of interest since it plays an important role in many computer vision applications. Existing works are mostly designed for outdoor tracking, such as video surveillance and autonomous driving. However, the behaviors of objects in outdoor tracking scenarios do not fully reflect the tracking challenges in indoor tracking environments. In outdoor tracking scenarios, pedestrians and vehicles usually move uniformly from place to place on a simple straight path, and target appearances are usually different. In contrast, in indoor scenarios, such as choreographed performances, the dynamic behaviors of dancers lead to severe occlusions, and similar costumes present a homogeneous appearance problem. These severe occlusion and homogeneous appearance problems in indoor tracking lead to noticeable degradation in the performance of existing works. In this paper, we propose a depth-enhanced tracking-by-detection framework and a semantic matching strategy combined with a scene-aware affinity measurement method to mitigate occlusion and homogeneous appearance problems significantly. In addition, we introduce an indoor tracking dataset and increase the diversity of existing benchmark datasets for indoor tracking evaluation. We conduct experiments on both the proposed indoor tracking dataset and the latest MOT benchmarks, MOT17 and MOT20. The experimental results show that our method consistently outperforms other works on the convincing HOTA metric across the benchmarks and greatly reduces the number of identity switches by 20% compared to that of the second-best tracker, DeepSORT, in our proposed indoor MOT benchmark dataset.