In the era of rapid technological advancement, the demand for sophisticated Multi-Object
Tracking (MOT) systems in applications such as intelligent surveillance and autonomous …

Real-time multi-object tracking (MOT) is a complex task involving detecting and tracking
multiple objects. After the objects are detected, they are assigned markers, and their …

Multi-object tracking (MOT) is pivotal for under-standing environments in which unmanned
vehicles function. The Joint Detection and Embedding (JDE) paradigm, merging target …

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 …

Deep learning models are known to function like the human brain. Due to their functional
mechanism, they are frequently utilized to accomplish tasks that require human intelligence …

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 …

Multi-object tracking is a vital component in various robotics and computer vision
applications. However, existing multi-object tracking techniques trade off computation …

Existing deep multi-object tracking (MOT) approaches first learn a deep representation to
describe target objects and then associate detection results by optimizing a linear …

In the realm of multimodal multi-object tracking (MOT) applications based on point clouds
and images, the current research predominantly focuses on enhancing tracking accuracy …

A robust MOT (multi-object tracking) is very crucial for computer vision applications such as
crowd density estimation and autonomous vehicles. Most of the existing mot approaches …