Multi-Object Tracking with Partial-Level Features and Adaptive Threshold Mechanism

Multi-object tracking (MOT) is a technique that aims to detect multiple objects in a single frame and maintain their identities in a given video sequence. Currently, the tracking-by-detection paradigm stands out as the most effective approach for MOT, encompassing object detection, person re-identification (Re-ID), and data association. In this paper, we propose enhancements to the Re-ID and data association modules, emphasizing the pivotal role of appearance information in achieving robust tracking quality. To capture more discriminative features, we introduce a network based on partial-level features. We also employ reparameterization to remove residual connection blocks from the backbone network without altering the original output to enhance tracking accuracy while optimizing computational efficiency. Moreover, the utilization of bounding boxes significantly influences tracking performance. We introduce an adaptive threshold mechanism and incorporate gating functions for data association, ensuring efficient use of each bounding box while minimizing trajectory fragmentation and missed detections. Experimental results illustrate that our proposed method attains high detection and tracking accuracy, particularly excelling in challenging video sequences. Our approach demonstrates comparable performance to state-of-the-art trackers on various benchmarks, including MOT17 and MOT20, where MOT20 presents frequent and severe interactions and occlusions.