2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 2 , PP: 303-323, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Veena R S 1 , Seema Rani 2 * , Ch Madhava Rao 3 , Piyush Kumar Pareek 4 , Sandeep Dalal 5 , Shweta Bansal 6
Doi: https://doi.org/10.54216/JISIoT.130224
Vehicle tracking is one of computer vision's most important applications, with applications ranging from robotics and traffic monitoring to autonomous vehicle navigation and many more. Even with the significant advancements in recent research, issues like occlusion, fluctuating illumination, and fast motion still need to be addressed, calling for more investigation and creativity in this field. This study performs a thorough examination of various vehicle-tracking approaches and suggests a thorough classification scheme that divides them into four main categories: strategies that rely on features, segmentation, estimate, or learning. Two well-known methods are highlighted specifically in the estimation-based category: particle filters and Kalman filters.
Vehicle Tracking , Kalman Filter , Particle filter , Computer vision , smart navigation system
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