In today’s world various industries, government offices, hospitals, educational institutions and public sector organizations face the challenge of effectively and accurately keeping records of vehicles entering and exiting their premises. Traditionally, this task has been carried out manually by writing down the information in a diary or similar format. However to address this issue, an efficient automated system has been developed that utilizes live camera surveillance technology. This system is designed to track the licence plate numbers of all vehicles entering and leaving the premises while maintaining a record of these activities. Alongside capturing licence plate numbers, this innovative system also automatically records the check in and check out times, for each vehicle. By doing it ensures a detailed log of all vehicles coming in and going out of the campus. The designed algorithm employed by this system guarantees that even high speed vehicle movements will not compromise the accuracy or precision of its tracking capabilities. To detect licence plates accurately, several deep learning algorithms like VGG-16, VGG-19, GoogLeNet, Alex Net and MobileNet are implemented in conjunction with Optical Character Recognition (OCR) technology for extracting text from these plates and classification. Through the implementation of this system, the entire tracking process is seamlessly digitized. This not reduces reliance, on manpower. Also significantly enhances security standards by enabling analytical tracking to identify vehicles that have left or remain on campus after check in. On analysis, it is greatly identified that GoogLeNet DNN method proposed has achieved an accuracy of 97.2% which’s significantly higher when compared to advanced methods, in the field.