Video Processing Based Tracking and Vehicle Identification

Abstract

Video surveillance and analysis have become integral components of various domains such as security, traffic management, and urban planning. However, effective tracking and identification of vehicles in video streams remain challenging due to environmental factors, occlusions, and complex motion patterns. This research proposes a novel approach leveraging YOLOv5, a state-of-the-art object detection algorithm, for real-time vehicle tracking and identification. By integrating YOLOv5 with advanced video processing techniques, including preprocessing for enhancing video quality and Kalman filtering for object tracking, the proposed system achieves improved accuracy and robustness in diverse scenarios. Experimental results demonstrate the effectiveness of the approach, showcasing high accuracy in vehicle tracking and reliable identification performance. The findings suggest significant potential for practical applications in enhancing video surveillance systems for better security and traffic management. Additionally, avenues for future research are discussed to further enhance the capabilities of video-based vehicle tracking and identification systems