Traditional multiple object tracking methods divide the task into two parts: affinity learning
and data association. The separation of the task requires to define a hand-crafted training …

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent
vehicle platform. At present, the main paradigm is tracking-by-detection, and the main …

The existing one-stage multi-object tracking (MOT) algorithms have achieved satisfactory
performance benefiting from a large amount of labeled data. However, acquiring plenty of …

Multi-object tracking (MOT) constructs multiple object trajectories by associating detections
between consecutive frames while maintaining object identities. In many autonomous …

Most modern multi‐object tracking (MOT) systems for videos follow the tracking‐by‐
detection paradigm, where objects of interest are first located in each frame then associated …

The popular tracking-by-detection paradigm of multi-object tracking (MOT) takes detections
of each frame as the input and associates detections from one frame to another. Existing …

Multiobject tracking (MOT) is a fundamental problem in computer vision with numerous
applications, such as intelligent surveillance and automated driving. Despite the significant …

In this work, we present an end-to-end framework to settle data association in online Multiple-
Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame data …

Driven by recent advances in object detection with deep neural networks, the tracking-by-
detection paradigm has gained increasing prevalence in the research community of multi …

Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in
accurately linking the trajectories of moving objects. Recently, modern deep learning models …