One-shot multiple object tracking (MOT), which learns object detection and identity
embedding in a unified network, has attracted increasing attention due to its low complexity …

Traditionally multi-object tracking and object detection are performed using separate
systems with most prior works focusing exclusively on one of these aspects over the other …

Multiple object tracking (MOT) is becoming increasingly significant for autonomous driving
and intelligent transportation systems. However, traditional MOT methods cannot track the …

Multi-object tracking (MOT) is one of the most challenging tasks in the field of computer
vision. Most MOT methods generally face the problem of not being able to handle pedestrian …

Abstract Generic Object Tracking (GOT) is the problem of tracking target objects, specified by
bounding boxes in the first frame of a video. While the task has received much attention in …

Multi-object tracking is one of the most fundamental problems in computer vision with wide
industrial applications. It involves the association of multiple targets across consecutive …

We describe a tracking-by-detection framework for multi-object tracking (MOT). It first detects
the objects of interest in each frame of the video, followed by identifying association with the …

Differentiable solvers for the linear assignment problem (LAP) have attracted much research
attention in recent years, which are usually embedded into learning frameworks as …

Multi-object tracking systems often consist of a combination of a detector, a short term linker,
a re-identification feature extractor and a solver that takes the output from these separate …

Data association plays a crucial role in Multi-Object Tracking (MOT), but it is usually
suppressed by occlusion. In this paper, we propose an online MOT approach via multiple …