SAMF-TLD Based Pedestrian Multi-Target Tracking - Liu, Bao; Zhao, Yuge; Yang, Haoning; Cao, Le (Proceedings (International Conference on Computer Engineering and Applications. Online), 2023)
Aiming at the tracking loss problem caused by target scale change and occlusion in pedestrian multi-target tracking, a scale adaptive kernel correlation filter with tracking-learning-detection (SAMF-TLD) based multi-target tracking method is proposed. This method combines a short-time tracker with the traditional track-detect-learn (TLD) multi-target tracking algorithm, which improves the real-time performance of the algorithm and overcomes the tracking failure problem of the TLD algorithm under illumination change and large scale change. Secondly, by judging the confidence of the tracking results of the scale adaptive kernel correlation filter (SAMF), the problem of updating the filter template when the target is occluded is solved, therefore, in order to improve the tracking efficiency, effectively avoid the tracking failure problems caused by the target being occluded and the tracking target scale change. In the experiment, 2DMOT-15 and MOT-16 data sets were selected for tracking verification, and the common evaluation indicators (MOTA, MOTP, MP, MT, and IDS) were compared and analyzed. The results show that the proposed method is superior to the TLD pedestrian multi-target tracking algorithm in the accuracy, and the SAMF tracking algorithm in terms of anti-occlusion.