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 …

The field of education has experienced a transformation as artificial intelligence (AI)
becomes increasingly applicable for learning purposes. AI has the potential to transform the …

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

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 …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

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

Tracking-by-detection is a common approach to multi-object tracking. With ever increasing
performances of object detectors, the basis for a tracker becomes much more reliable. In …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

In the recent past, the computer vision community has developed centralized benchmarks
for the performance evaluation of a variety of tasks, including generic object and pedestrian …

Effective multi-object tracking (MOT) methods have been developed in recent years for a
wide range of applications including visual surveillance and behavior understanding …