Rt-Track: Robust Tricks for Multi-Pedestrian Tracking
It addresses challenges in multi-pedestrian tracking for video analysis, offering incremental improvements over existing methods.
The paper tackles the problem of multi-object tracking in complex scenes by proposing Rt-Track, a robust tracker that incorporates novel methods for motion and appearance modeling, achieving state-of-the-art performance with 79.5 MOTA, 76.0 IDF1, and 62.1 HOTA on MOT17, and 77.9 MOTA, 78.4 IDF1, and 63.3 HOTA on MOT20.
Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT). MOT aims to maintain the identities of multiple objects across a series of continuous video sequences. In recent years, MOT has made rapid progress. However, modeling the motion and appearance models of objects in complex scenes still faces various challenging issues. In this paper, we design a novel direction consistency method for smooth trajectory prediction (STP-DC) to increase the modeling of motion information and overcome the lack of robustness in previous methods in complex scenes. Existing methods use pedestrian re-identification (Re-ID) to model appearance, however, they extract more background information which lacks discriminability in occlusion and crowded scenes. We propose a hyper-grain feature embedding network (HG-FEN) to enhance the modeling of appearance models, thus generating robust appearance descriptors. We also proposed other robustness techniques, including CF-ECM for storing robust appearance information and SK-AS for improving association accuracy. To achieve state-of-the-art performance in MOT, we propose a robust tracker named Rt-track, incorporating various tricks and techniques. It achieves 79.5 MOTA, 76.0 IDF1 and 62.1 HOTA on the test set of MOT17.Rt-track also achieves 77.9 MOTA, 78.4 IDF1 and 63.3 HOTA on MOT20, surpassing all published methods.