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 …

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …

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 …

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about
track initialization, identity, and spatio-temporal trajectories. We formulate this task as a …

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 …

Most online multi-object trackers perform object detection stand-alone in a neural net without
any input from tracking. In this paper, we present a new online joint detection and tracking …

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 …