Multi-object tracking (MOT) in video sequences remains a challenging task, especially in
scenarios with significant camera movements. This is because targets can drift considerably …

Existing multiple object tracking methods usually strengthen data association by
discriminative identity embeddings. However, many works treat object detection and …

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 …

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …

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 …

In Multiple Object Tracking (MOT), tracking-by-detection methods have stood the test for a
long time, which split the process into two parts according to the definition: object detection …

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 …

Although refinement is commonly used in visual tasks to improve pre-obtained results, it has
not been studied for Multiple Object Tracking (MOT) tasks. This could be attributed to two …

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging
the representational power of deep learning to jointly learn to detect and track objects …

Without manually annotated identities, unsupervised multi-object trackers are inferior to
learning reliable feature embeddings. It causes the similarity-based inter-frame association …