The challenge of multi-object tracking stands as a fundamental focus in computer vision
research, finding widespread applications in areas such as public safety, transportation …

Deep learning-based object detectors have driven notable progress in multi-object tracking
algorithms. Yet, current tracking methods mainly focus on simple, regular motion patterns in …

Tracking multiple pedestrians across multi-camera scenarios is an important part of
intelligent video surveillance and has great potential application for public security, which …

While most multi-object tracking methods based on tracking-by-detection use either spatial
or appearance cues for associating detections or apply one cue after another, our proposed …

Due to balanced tracking accuracy and speed, Joint-detection-and-embedding (JDE)
tracking paradigm has drawn great attention, which employs a single work to predict …

Multi-Object Tracking (MOT) has gained extensive attention in recent years due to its
potential applications in traffic and pedestrian detection. We note that tracking by detection …

Pedestrian detection and tracking are essential problems in the field of computer vision,
having a wide range of applications in surveillance, security, autonomous driving and …

Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT).
MOT aims to maintain the identities of multiple objects across a series of continuous video …

In computer vision, multiple object tracking (MOT) plays a crucial role in solving many
important issues. A common approach of MOT is tracking by detection. Tracking by detection …

Pedestrian detection and tracking are critical functions in the application of computer vision
for autonomous driving in terms of accident avoidance and safety. Extending the application …