SolidTrack: A Novel Method for Robust and Reliable Multi-Pedestrian Tracking

Multi-Object Tracking (MOT) remains a fundamental challenge in computer vision, with significant applications in autonomous systems, surveillance, and intelligent transportation. The Tracking-By-Detection (TBD) paradigm has achieved notable tracking accuracy; however, its performance is highly sensitive to low-confidence detections (e.g., partial occlusions or blurred objects). Existing methods, such as ByteTrack, utilize a dual-threshold strategy for the filtering of detections, but determining the optimal high-confidence threshold is computationally expensive and lacks robustness across different scenarios. In this study, we analyze the sensitivity of state-of-the-art (SOTA) TBD-based trackers to variations in the high-confidence threshold and introduce SolidTrack, an enhanced tracking algorithm integrating SOLIDER-ReID, a transformer-based re-identification model. Our approach mitigates the impact of threshold variations by improving feature extraction, particularly under challenging conditions such as low-confidence detections and occlusions. Experimental evaluations demonstrate that SolidTrack exhibits superior robustness across varying threshold settings. Additionally, SolidTrack achieves SOTA performance on the test sets of the MOT17 and MOT20 benchmarks, achieving an MOTA of 80.6, IDF1 of 80.0, and HOTA of 65.0 on MOT17 and an MOTA of 77.9, IDF1 of 77.5, and HOTA of 63.4 on MOT20.