Journal of Advanced Research in Applied Sciences and Engineering Technology 40, Issue 2 (2024) 221-241 221 Journal of Advanced Research in Applied Sciences and Engineering Technology Journal homepage: https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index ISSN: 2462-1943 Automated Recognition and Tracking of Stationary and Moving Cars in Images and Videos: YOLOv5 and SSD Analysis Priyanka Ankireddy 1,* , S. Gopalakrishnan 1 , V. Lokeswara Reddy 2 1 2 Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, Tamilnadu-603103, India Department of Computer science and Engineering, KSRM College of Engineering, Kadapa, Andhra Pradesh-516003, India ARTICLE INFO ABSTRACT Article history: Received 3 September 2023 Received in revised form 20 November 2023 Accepted 5 January 2024 Available online 28 February 2024 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%. Keywords: Vehicle detection; Image processing; Vehicle tracking; Deep learning; Object tracking 1. Introduction Automatic vehicle data recognition has found use in several contexts, including the vehicle information system and the intelligent traffic system. Because of advancements in digital picture technology and advances in processing capability, it has drawn considerable attention from academics since the beginning of this decade. Automatic vehicle detection systems are a fundamental component of many modern traffic management applications [1,2]. As the population and the infrastructure that supports it continue to rise, so too does the pressure on those responsible for their management. The world's population is growing at an incredible rate. The result was a surge in the production of vehicles and other mechanical devices. However, the cautious management of emerging issues including traffic, accidents, and a wide range of other concerns is essential. It's * Corresponding author. E-mail address: priyasivakrishna99@gmail.com https://doi.org/10.37934/araset.40.2.221241