Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding
autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms …

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

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 …

Abstract Multiple Object Tracking (MOT) has emerged as a hot issue in the field of computer
vision recently. MOT has academic and commercial potential in urban public security …

We present a unified method, termed Unicorn, that can simultaneously solve four tracking
problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters …

Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …

Transformer architecture has been showing its great strength in visual object tracking, for its
effective attention mechanism. Existing transformer-based approaches adopt the pixel-to …

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 …

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 …