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 …

There has been an exponential growth in the amount of visual data on a daily basis
acquired from single or multi-view surveillance camera networks. This massive amount of …

A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization,
and following re-identification (re-ID) for object association. This pipeline is partially …

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 …

Most recent successes on forecasting the people motion are based on LSTM models and all
most recent progress has been achieved by modelling the social interaction among people …

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

Multi-object tracking (MOT) in sports scenes plays a critical role in gathering players
statistics, supporting further applications, such as automatic tactical analysis. Yet existing …

With the advantage of high mobility, Unmanned Aerial Vehicles (UAVs) are used to fuel
numerous important applications in computer vision, delivering more efficiency and …

In this paper we present a large-scale visual object detection and tracking benchmark,
named VisDrone2018, aiming at advancing visual understanding tasks on the drone …

Tracking objects in soccer videos is extremely important to gather both player and team
statistics, whether it is to estimate the total distance run, the ball possession or the team …