Multiple Object Tracking (MOT) plays an important role in solving many fundamental
problems in video analysis and computer vision. Most MOT methods employ two steps …

Despite recent progress in Multiple Object Tracking (MOT), several obstacles such as
occlusions, similar objects, and complex scenes remain an open challenge. Meanwhile, a …

Data association-based multiple object tracking (MOT) involves multiple separated modules
processed or optimized differently, which results in complex method design and requires …

Traditional multiple object tracking methods divide the task into two parts: affinity learning
and data association. The separation of the task requires to define a hand-crafted training …

For a long time, the most common paradigm in MultiObject Tracking was tracking-by-
detection (TbD), where objects are first detected and then associated over video frames. For …

Detection and learning based appearance feature play the central role in data association
based multiple object tracking (MOT), but most recent MOT works usually ignore them and …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. However, it is not trivial to solve the data …

Similarity learning has been recognized as a crucial step for object tracking. However,
existing multiple object tracking methods only use sparse ground truth matching as the …

Multi-object tracking in unmanned aerial vehicle (UAV) videos is an important vision task
and can be applied in a wide range of applications. However, conventional multi-object …

Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …