TY  - EJOU
AU  - Li, Suya 
AU  - Cao, Ying 
AU  - Ren, Hengyi 
AU  - Zhu, Dongsheng 
AU  - Xie, Xin 

TI  - LQTTrack: Multi-Object Tracking by Focusing on Low-Quality Targets Association
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 1
SN  - 1546-2226

AB  - Multi-object tracking (MOT) has seen rapid improvements in recent years. However, frequent occlusion remains a significant challenge in MOT, as it can cause targets to become smaller or disappear entirely, resulting in low-quality targets, leading to trajectory interruptions and reduced tracking performance. Different from some existing methods, which discarded the low-quality targets or ignored low-quality target attributes. LQTTrack, with a low-quality association strategy (LQA), is proposed to pay more attention to low-quality targets. In the association scheme of LQTTrack, firstly, multi-scale feature fusion of FPN (MSFF-FPN) is utilized to enrich the feature information and assist in subsequent data association. Secondly, the normalized Wasserstein distance (NWD) is integrated to replace the original Inter over Union (IoU), thus overcoming the limitations of the traditional IoU-based methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target tracking. Moreover, the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking, thereby increasing the association success rate and improving overall multi-object tracking performance. Extensive experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets (MOT17, MOT20, and DanceTrack).
KW  - Low-quality targets association strategy; feature fusion; multi-object tracking; tracking-by-detection

DO  - 10.32604/cmc.2024.056824