Multi-Object Tracking
This special issue belongs to the section "Computer Vision and Pattern Recognition".
Special Issue Information
Dear Colleagues,
Over the past three decades, the computer vision community has developed several methods, such as multi-object tracking (MOT), whose many challenges stem from the rapid expansion of its application areas in video surveillance, robotic vision, autonomous vehicles and object-of-interest tracking, indoor navigation, smart airport security, unmanned stores, etc. MOT is used to address illumination variations, fast motion, tracking performance, as well as challenges in tracking with occlusions between objects. MOT is always seeking quasi-optimized solutions as well as higher accuracy in this continuously expanding area. Practical technical methods as well as theories regarding the tracking of objects, and promising directions for success for this area of research are the subjects of this Special Issue on MOT techniques and applications.
Prof. Dr. Phill Kyu Rhee
Dr. Daniel Howard
Guest Editors
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Keywords
- multi-object tracking (MOT)
- deep learning
- machine learning
- generative learning
- discriminative learning
- reinforcement learning
- online deep learning
- real-time MOT
- feature-based MOT
- segmentation-based MOT
- estimation-based MOT
- learning-based MOT
- video surveillance
- robotic vision
- autonomous vehicle tracking
- indoor navigation
- smart airport security
- unmanned store
- Bayesian, heuristic, bioinspired, or any techniques that can achieve MOT
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Published Papers
J. Imaging - ISSN 2313-433X