Recent developments in video analysis of sports and computer vision techniques have
achieved significant improvements to enable a variety of critical operations. To provide …

Environment perception, one of the most fundamental and challenging problems of
autonomous vehicles (AVs), has been widely studied in recent decades. Due to inferior fault …

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

The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene,
while keeping a unique identifier for each object. In this paper, we present a new robust …

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 …

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 …

Standardized benchmarks have been crucial in pushing the performance of computer vision
algorithms, especially since the advent of deep learning. Although leaderboards should not …

Abstract 3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and
approaches in recent years, especially those under the “tracking-by-detection” paradigm …

Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …

Deep learning-based methods for video pedestrian detection and tracking require large
volumes of training data to achieve good performance. However, data acquisition in …