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 …

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 …

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 …

Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …

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 …

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 …

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide
range of applications, including agriculture, aerial photography, and surveillance …