Multi-object tracking (MOT) is a significant challenge within the field of computer vision,
holding various practical applications. Nevertheless, it is still a challenge to extract more …

Multi-object tracking (MOT) is one of the most challenging tasks in the field of computer
vision. Most MOT methods generally face the problem of not being able to handle pedestrian …

Traditional multiple object tracking methods divide the task into two parts: affinity learning
and data association. The separation of the task requires to define a hand-crafted training …

Online multi-object tracking is a process of extending multi-object trajectories with only past
information. In this process, tracking drift, missing detection, and occlusion among objects …

Propagation and association tasks in Multi-Object Tracking (MOT) play a pivotal role in
accurately linking the trajectories of moving objects. Recently, modern deep learning models …

Abstract Multi-Object Tracking (MOT) is a challenging research area in computer vision with
significant practical applications. With the advent of deep neural networks, significant …

Recently, there has been a surge of interest in using one-shot methods for multi-object
tracking (MOT). These methods use a single network to produce both object detection …

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 …

Most of the existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-
Detection and Data Association paradigm, in which objects are firstly detected and then …

Data association plays a crucial role in Multi-Object Tracking (MOT), but it is usually
suppressed by occlusion. In this paper, we propose an online MOT approach via multiple …