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 …

Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …

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 …

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

Humans navigate complex crowded environments based on social conventions: they
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …

The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine
cues over a long period of time in a coherent fashion. In this paper, we present an online …

Tracking objects over long videos effectively means solving a spectrum of problems, from
short-term association for un-occluded objects to long-term association for objects that are …

In this paper, we propose a CNN-based framework for online MOT. This framework utilizes
the merits of single object trackers in adapting appearance models and searching for target …

In recent years, algorithms for multiple object tracking tasks have benefited from great
progresses in deep models and video quality. However, in challenging scenarios like drone …