Multi-object tracking (MOT) is a vital component of intelligent video analytics applications
such as surveillance and autonomous driving. The time and storage complexity required to …

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging
the representational power of deep learning to jointly learn to detect and track objects …

Multi-object tracking has recently become an important area of computer vision, especially
for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high …

For a long time, the most common paradigm in MultiObject Tracking was tracking-by-
detection (TbD), where objects are first detected and then associated over video frames. For …

In this work, we focus on improving online multi-object tracking (MOT). In particular, we
propose a novel region-based Siamese Multi-Object Tracking network, which we name …

Tracking by detection paradigm is one of the most popular object tracking methods.
However, it is very dependent on the performance of the detector. When the detector has a …

In recent years, tracking-by-detection has become the most popular multi-object tracking
(MOT) method, and deep convolutional neural networks (CNNs)-based appearance features …

Multi-object tracking (MOT) is one of the significant directions of computer vision. Though
existing methods can solve simple tasks like pedestrian tracking well, some complex …

Multiple Object Tracking (MOT) plays an important role in solving many fundamental
problems in video analysis and computer vision. Most MOT methods employ two steps …

Real-time multiple object tracking plays a pivotal role in autonomous driving applications,
particularly in real-world applications. Current methods in various domains often face an …