The advances of Visual object tracking tasks in computer vision have enabled a growing
value in its application to video surveillance, particularly in a traffic scenario. In recent years …

Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …

Modern multi-object tracking (MOT) systems usually build trajectories through associating
per-frame detections. However, facing the challenges of camera motion, fast motion, and …

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT),
which often leads to inferior tracking results due to the missing detected objects. The …

The one-shot multi-object tracking, which integrates object detection and ID embedding
extraction into a unified network, has achieved groundbreaking results in recent years …

This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature …

Tracking both location and pose of multiple planar objects (MPOT) is of great significance to
numerous real-world applications. The greater degree-of-freedom of planar objects …

Multi-object tracking (MOT) has become a hot task in multi-media analysis. It not only locates
the objects but also maintains their unique identities. However, previous methods encounter …

The data association problem of multi-object tracking (MOT) aims to assign IDentity (ID)
labels to detections and infer a complete trajectory for each target. Most existing methods …

The ability to track multiple objects is crucial for roadside units to provide high-precision,
trajectory-based traffic data, especially for connected vehicles that require complementary …