A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

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 …

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

We propose an online tracking algorithm that performs the object detection and data
association under a common framework, capable of linking objects after a long time span …

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …

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

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

Object detection and data association are critical components in multi-object tracking (MOT)
systems. Despite the fact that the two components are dependent on each other, prior works …

In this work, we focus on improving online multi-object tracking (MOT). In particular, we
propose a novel region-based Siamese Multi-Object Tracking network, which we name …

Multi-pedestrian trackers perform well when targets are clearly visible making the
association task quite easy. However, when heavy occlusions are present, a mechanism to …