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 …

Object segmentation and object tracking are fundamental research areas in the computer
vision community. These two topics are difficult to handle some common challenges, such …

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 …

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

Tracking has traditionally been the art of following interest points through space and time.
This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by …

Automatically generating high-quality real world 3D scenes is of enormous interest for
applications such as virtual reality and robotics simulation. Towards this goal, we introduce …

Abstract Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-
detection paradigm to conduct object detection, feature extraction and data association …

Tracking multiple objects in videos relies on modeling the spatial-temporal interactions of
the objects. In this paper, we propose TransMOT, which leverages powerful graph …

The problem of tracking multiple objects in a video sequence poses several challenging
tasks. For tracking-by-detection, these include object re-identification, motion prediction and …

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-
detection paradigm. However, they also introduce a major challenge for learning methods …