Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …

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 …

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 …

This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

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 …

Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …

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 …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

As an important area in computer vision, object tracking has formed two separate
communities that respectively study Single Object Tracking (SOT) and Multiple Object …