Based on the detection technology, regressing predicted bounding boxes provides an
effective approach in multiple object tracking. However, if only the information in the current …

Multi-object tracking (MOT) is an important topic and critical task related to both static and
moving camera applications, such as traffic flow analysis, autonomous driving and robotic …

We propose a conceptually simple and thus fast multi-object tracking (MOT) model that does
not require any attached modules, such as the Kalman filter, Hungarian algorithm …

Online multi-object tracking (MOT) is a fundamental problem in video analysis and
multimedia applications. The major challenge in the popular tracking-by-detection …

Most existing multi-object tracking methods typically learn visual tracking features via
maximizing dis-similarities of different instances and minimizing similarities of the same …

Automatically detecting, labeling, and tracking objects in videos depends first and foremost
on accurate category-level object detectors. These might, however, not always be available …

Multiple object tracking (MOT) is a crucial task in computer vision society. However, most
tracking-by-detection MOT methods, with available detected bounding boxes, cannot …

Multi-object tracking (MOT) has long been a crucial topic in the field of autonomous driving
and security monitoring. With the saturation of the bounding-box-based MOT algorithms in …

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 …

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 …