Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of
the objects. In this paper, we propose TransMOT, which leverages powerful graph …

Abstract 3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and
approaches in recent years, especially those under the “tracking-by-detection” paradigm …

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-
detection paradigm. However, they also introduce a major challenge for learning methods …

In this work, we propose a method that combines a single hand-held camera and a set of
Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

Tracking objects over long videos effectively means solving a spectrum of problems, from
short-term association for un-occluded objects to long-term association for objects that are …

Abstract Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video
taken from several cameras. Person Re-Identification (Re-ID) retrieves from a gallery images …

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a
new pair of precision-recall measures of performance that treats errors of all types uniformly …