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 …

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 …

In the last few years, enormous strides have been made for object detection and data
association, which are vital subtasks for one-stage online multi-object tracking (MOT) …

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 …

The anchor-free one-shot models, which localize the detections and extract embeddings by
estimating center points in a single network have been proven highly effective in multi-object …

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 …

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 …

Discriminative representation is crucial for the association step in multi-object tracking.
Recent work mainly utilizes features in single or neighboring frames for constructing metric …

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

Online multi-object tracking aims at generating the trajectories for multiple objects in the
surveillance scene. It remains a challenging problem in crowded scenes because objects …