The nascent applicability of multi-camera tracking (MCT) in numerous real-world
applications makes it a significant computer vision problem. While visual tracking of objects …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Video object segmentation (VOS) aims at segmenting a particular object throughout the
entire video clip sequence. The state-of-the-art VOS methods have achieved excellent …

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 …

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 …

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

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …

Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …