Multi-object tracking (MOT) is a critical task in various domains, such as traffic analysis,
surveillance, and autonomous vehicles. The joint-detection-and-tracking paradigm has been …

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple
object tracking problems. TransTrack leverages the transformer architecture, which is an …

Abstract Deep learning based Multiple Object Tracking (MOT) currently relies on off-the-shelf
detectors for tracking-by-detection. This results in deep models that are detector biased and …

Multi-object tracking (MOT) requires detecting and associating objects through frames.
Unlike tracking via detected bounding boxes or center points, we propose tracking objects …

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 …

Existing multi-object trackers mainly apply the tracking-by-detection (TBD) paradigm and
have achieved remarkable success. However, the mainstream methods execute their …

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 recent years, dominant multi-object tracking (MOT) and segmentation (MOTS) methods
mainly follow the tracking-by-detection paradigm. Transformer-based end to end (E2E) …

Abstract Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception. End-to-
end transformer-based algorithms, which detect and track objects simultaneously, show …

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 …