Multi-object tracking is a research hotspot in intelligent driving scenarios, and the trajectory
association of tracked objects is one of the hotspots that needs to be solved urgently. The …

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 …

In recent years, multi-object tracking has attracted more and more attention, both in
academia and engineering, but most of the recent works do not pay attention to the speed of …

Multiobject tracking (MOT) in video sequences has gradually become one of the most
essential fields in computer vision tasks. As the use of two separate models for feature …

Multi-object Tracking is an important issue that has been widely investigated in computer
vision. However, in practical applications, moving targets are often occluded due to complex …

Detection plays an important role in improving the performance of multi-object tracking
(MOT), but most recently MOT works mainly focus on association algorithm and usually …

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches
due to the success of convolutional neural networks (CNNs) on detection in the last decade …

The performance of pedestrian multiple object tracking (MOT), which is based on the
tracking-by-detection framework, is exceedingly susceptible to the quality of detection …

Most current online multi-object tracking (MOT) methods include two steps: object detection
and data association, where the data association step relies on both object feature …

Multi-object tracking in dense scenes has always been a major difficulty in this field.
Although some existing algorithms achieve excellent results in multi-object tracking, they fail …