We present a novel transformer-based architecture for global multi-object tracking. Our
network takes a short sequence of frames as input and produces global trajectories for all …

Autonomous robotic systems operating in human environments must understand their
surroundings to make accurate and safe decisions. In crowded human scenes with close-up …

Transformers have proven superior performance for a wide variety of tasks since they were
introduced. In recent years, they have drawn attention from the vision community in tasks …

Mainstream multi-object tracking methods exploit appearance information and/or motion
information to achieve interframe association. However, dealing with similar appearance …

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

3D single object tracking is a key task in 3D computer vision. However, the sparsity of point
clouds makes it difficult to compute the similarity and locate the object, posing big …

Abstract Multi-Object Tracking (MOT) has been one of the most important topics in computer
vision. The traditional tracking-by-detection framework of MOT is severely suffered from the …

Maintaining identity consistency and avoiding ID-switch during tracking is one of the primary
focuses of multiple object tracking (MOT). One-shot MOT methods which jointly learn the …

This survey covers an in-depth review of the state-of-the-art research on Multi-Object
Tracking (MOT) from research articles published in 2019 or later in top-tier journals and …

Multiple object tracking (MOT), as a typical application scenario of computer vision, has
attracted significant attention from both academic and industrial communities. With its rapid …