Optimized Vehicle Recovery: Leveraging AI and CCTV for Effective Tracking

Abstract

Vehicle theft is a rising crime across the globe. GPS trackers are not a standard fit in all vehicles and the lack of internet connectivity makes tracking down stolen vehicles challenging for officials. GPS signals can be affected by different environmental factors such as terrain, or dense foliage, which can cause inaccuracies in location tracking. In order to address these problems, the suggested solution makes use of the CCTV system to follow the route of a stolen car and speed up the search effort. The suggested concept presents a successful method for detecting and monitoring stolen cars through CCTV videos, utilizing advanced computer vision and deep learning methods. Reviewing the CCTV footage from various sources can expedite the reaction and improve the retrieval while upholding data privacy. The accuracy of predicting car color and car model was above 90% in the results. The car routes are forecasted with RMSE score of 0.000171. The suggested model proposes a tracking solution that utilizes the current CCTV infrastructure without requiring any extra hardware to be installed in the vehicles.