Multi-object Tracking (MOT) is very important in human surveillance, sports analytics,
autonomous driving, and cooperative robots. Current MOT methods do not perform well in …

This paper introduces temporally local metrics for Multi-Object Tracking. These metrics are
obtained by restricting existing metrics based on track matching to a finite temporal horizon …

Most online multi-object tracking methods utilize bounding boxes and center points inherited
from detectors as the base models to represent targets. Limited performance is obtained with …

Multi-object Tracking (MOT) generally can be split into two sub-tasks, ie, detection and
association. Many previous methods follow the tracking by detection paradigm, which first …

Multi-Object Tracking (MOT) is a cornerstone operator for video surveillance applications. To
enable real-time processing of large-scale live video streams, we study an interesting …

Multi-object tracking (MOT) requires detecting and associating objects through frames.
Unlike tracking via detected bounding boxes or center points, we propose tracking objects …

Discriminative representation is essential to keep a unique identifier for each target in
Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding …

In detection-based multi-object tracking (MOT), one challenging problem is to design a
robust affinity model for data association. Moreover, since these approaches entirely rely on …

Accurate data association is crucial in reducing confusion such as ID switches and
assignment errors in multi-object tracking (MOT). However existing advanced methods often …

Although the joint-detection-and-tracking paradigm has promoted the development of multi-
object tracking (MOT) significantly, the long-term occlusion problem is still unsolved. After a …