PreAFTrack: Multi-Object Tracking Based on Adaptive Feature Matching from Detection Results

Most two-stage detection and tracking methods currently adopt IoU distance and appearance features when matching target detection results with trajectory predictions. However, this approach has shortcomings in two main aspects: 1. The selection of feature extraction networks often relies on established methods, such as Re-ID-related methods, which are typically complex and significantly slow down algorithm processing speed. 2. Feature extraction networks lack consistency when extracting single-frame features, leading to difficulties in matching incomplete objects with previous features when occlusions occur, resulting in incorrect updates of target identities. To address these limitations, this study introduces a lightweight feature extraction network that combines current target features with preceding information for fusion. This network enhances data processing speed while obtaining smooth object features with temporal coherence. Moreover, considering variations in feature granularity among different targets, various feature matching approaches are employed to handle detection boxes under different conditions, maximizing information efficiency and conserving computational resources. An adaptive multi-feature extraction method is also proposed to accommodate diverse requirements across different scenarios. The matching strategy developed allows the tracker to serve as a universal and practical solution for various tracking scenarios within a unified framework, eliminating the need for manual parameter tuning. The effectiveness of PreAFTrack is demonstrated through comprehensive evaluations on three major MOT datasets: MOT17, MOT20, and DanceTrack.