Automated Recognition and Tracking of Stationary and Moving Cars in Images and Videos: YOLOv5 and SSD Analysis

Abstract

The detection and tracking of cars are a significant and useful aspect of traffic surveillance systems, which is essential for the efficient management of traffic and the security of drivers and passengers. The primary objectives of this investigation are vehicle detection and tracking. The automatic detection of cars in both still photos and video recordings are the main topic of this investigation. One of the many uses for Deep Learning, which combines fuzzy logic, neural networks, and evolutionary algorithms, is the detection and tracking of moving objects. In this work, the key object detection techniques YOLOv5 and SSD were used to analyse vehicle recognition and tracking with deep learning. The Single Shot Multi-Box Detector model architecture is then utilized as the main foundation for the detection of vehicles. The vehicle recognition model is then trained using the YOLOv5 and SSD algorithms, each of which contributes to the illustration of the detection effect. Comparing the detection rates obtained by both models on a range of cars is necessary to locate it. The objective of this work is to create an automated system for locating and tracking both moving and stationary cars in still images and moving movies. According to the research, using this method has improved the success rate of recognizing automobiles to 97.65%.