Existing multi-object trackers mainly apply the tracking-by-detection (TBD) paradigm and
have achieved remarkable success. However, the mainstream methods execute their …

The stable continuation of trajectories among different targets has always been the key to
the tracking performance of multi-object tracking (MOT) tasks. If features of the target are …

Recently, there has been a surge of interest in using one-shot methods for multi-object
tracking (MOT). These methods use a single network to produce both object detection …

Despite its recent advancements, multi-object tracking (MOT), one of the major research
areas in video technology, still faces various challenges, including severe occlusion and …

Mainstream multiobject tracking (MOT) algorithms employ global object detection and
association methods. However, when dealing with scenarios involving crowded tiny objects …

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

Multi-object trackers make the association almost perfectly when no occlusion occurred
between two or more targets. However, it is hard to extract reliable features on account of …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. In this paper, we propose a novel solution …

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 …

Most existing one-shot multi-object tracking (MOT) methods have already made great
progress in jointly accomplishing detection and re-identification tasks with a single network …