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

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

Multiple Object Tracking (MOT) plays an important role in solving many fundamental
problems in video analysis and computer vision. Most MOT methods employ two steps …

In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates
the merits of single object tracking and data association methods in a unified framework to …

The rapid advances of transportation infrastructure have led to a dramatic increase in the
demand for smart systems capable of monitoring traffic and street safety. Fundamental to …

In this paper, we propose a multi-level cooperative fusion approach to address the online
multiple human tracking problem in a Gaussian mixture probability hypothesis density (GM …

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is
implemented, the similarity metric between the current detections and existing tracks plays …

An enhanced sequential Monte Carlo probability hypothesis density (PHD) filter-based
multiple human tracking system is presented. The proposed system mainly exploits two …

This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to
multi-object tracking in video data. In order to take advantage of additional visual …

In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker
to multi-object tracking. For each object, we use an individual tracker to estimate the …