As a crucial component in the realm of computer vision, multi-object tracking has garnered
widespread application in areas such as autonomous driving, smart transportation, and …

Due to the problem of low tracking accuracy and weak tracking stability of current multi-
object pedestrian tracking algorithms in complex scenes for videos, a Detection-based …

Object tracking is a crucial research area within the field of intelligent transportation,
providing a vital foundation for anomalous behavior analysis and traffic statistics. Although …

Multiobject tracking (MOT) is a fundamental problem in computer vision with numerous
applications, such as intelligent surveillance and automated driving. Despite the significant …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

Pedestrian target tracking is an important problem in the field of computer vision. To address
low tracking accuracy and tracking errors in pedestrian target tracking. This paper …

Deep learning methods have led to remarkable progress in multiple object tracking (MOT).
However, when tracking in crowded scenes, existing methods still suffer from both …

In order to improve the tracking failure caused by small-target pedestrians and partially
blocked pedestrians in dense crowds in complex environments, a pedestrian target …

Most multi-object tracking methods have achieved good results in tracking multiple
pedestrians with Kalman filter, but their tracking performance in crowded scenes is still poor …

Though achieving aggressive progress, there are only a few explorations on the robustness
of Multi-Object Tracking (MOT) trackers. Most of the existing MOT research focuses on …