Objective of multiple object tracking (MOT) is to assign a unique track identity for all the
objects of interest in a video, across the whole sequence. Tracking-by-detection is the most …

The advances of Visual object tracking tasks in computer vision have enabled a growing
value in its application to video surveillance, particularly in a traffic scenario. In recent years …

Multiple object tracking (MOT) aims to model the temporal relationship among detected
objects and associate them into trajectories. Thus, one major challenge of MOT lies in the …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking
(MOT) for its concise pipeline. Many current works first associate the detections to form track …

Recently, with the development of deep-learning, the performance of multi-object tracking
algorithms based on deep neural networks has been greatly improved. However, most …

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT)
algorithms have to deal with. Many state-of-the-art approaches now use sequence models to …

Detection and learning based appearance feature play the central role in data association
based multiple object tracking (MOT), but most recent MOT works usually ignore them and …

Multi-object tracking (MOT) has wide applications in the fields of video analysis and signal
processing. A major challenge in MOT is how to associate the noisy detections into long and …