In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker
to multi-object tracking. For each object, we use an individual tracker to estimate the …

Multiple-object tracking is a fundamental computer vision task which is gaining increasing
attention due to its academic and commercial potential. Multiple-object detection …

The problem of occlusion occurs during multi-target tracking may result in loss of
characteristics of tracking target and thus lose the tracking targets. This paper proposes a …

Multi-object tracking is a key research problem in computer vision area, and with the fast
development of the deep learning based image and video processing algorithms, the …

Aiming at the problems of ID switching and tracking performance degradation caused by
frequent occlusion and similar appearance of the tracked objects in dense scenes, a multi …

Most existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-Detection
paradigm and the data association framework where objects are firstly detected and then …

In the Multi-Object Tracking (MOT) scenario, on top of existing image-based approaches, 3D
information can also be essential to improving tracking performance. This paper introduces …

Computer Vision has set a new trend in image resolution, object detection, object tracking,
and more by incor-porating advanced techniques from Artificial Intelligence (AI). Object …

Multi-object pedestrian tracking plays a crucial role in autonomous driving systems, enabling
accurate perception of the surrounding environment. In this paper, we propose a …

Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding
autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms …