LAUSR

Conventional multi-object tracking (MOT) methods often fail to track objects that repeatedly disappear and reappear in the scene due to occlusion or moving off the screen. Previous studies have used motion and appearance information to address these issues but have limitations in dealing with complex scenarios. To overcome these limitations, we propose a post-hoc framework for MOT, a so-called rethinking MOT. The proposed framework efficiently combines MOT and re-identification methods to handle tracking failures in conventional MOT methods. To this end, we proposed a track reliability checking method to determine unreliable tracks and performed re-identification to correct incorrect tracking results. For re-identification, we proposed appearance aspect-ratio matching that is practical and effective. In addition, we designed an online appearance model management for accurate and efficient re-identification. The proposed framework does not require any offline training stages, such as feature or metric learning, but it is very practical for comparing appearances in real time. In addition, it is a flexible and pluggable framework that can adopt any baseline methods for MOT and Re-id. To validate the proposed methods, we collected 12 new challenging indoor datasets. Unlike the existing benchmark MOT datasets, we consider complex indoor sequences where targets reappear many times in the scene. The experimental results reveal that the proposed methods are promising for MOT under various challenging sequences and outperform state-of-the-art methods. Compared to baseline of the proposed rethinking MOT, the $IDF_{1}$ score was increased by 36.9% in own dataset and 50.4% in the benchmark dataset.