Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding
autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms …

Multiple animal tracking based on deep learning is a crucial and demanding task with
extensive applications in agriculture, livestock farming, and ecological research. The primary …

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …

In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end
multi-object tracking with a pretrained object detector. Existing end-to-end methods, eg …

Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved
prominence with the rise of powerful object detectors. Despite this, little work has been done …

The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …

A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization,
and following re-identification (re-ID) for object association. This pipeline is partially …

As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information
of targets effectively. Unfortunately, most existing methods only explicitly exploit the object …

Multi-object tracking (MOT) in sports scenes plays a critical role in gathering players
statistics, supporting further applications, such as automatic tactical analysis. Yet existing …