An Automated Precise Authentication of Vehicles for Enhancing the Visual Security Protocols

Abstract

The movement of vehicles in and out of the predefined enclosure is an important security protocol that we encounter daily. Identification of vehicles is a very important factor for security surveillance. In a smart campus concept, thousands of vehicles access the campus every day, resulting in massive carbon emissions. Automated monitoring of both aspects (pollution and security) are an essential element for an academic institution. Among the reported methods, the automated identification of number plates is the best way to streamline vehicles. The performances of most of the previously designed similar solutions suffer in the context of light exposure, stationary backgrounds, indoor area, specific driveways, etc. We propose a new hybrid single-shot object detector architecture based on the Haar cascade and MobileNet-SSD. In addition, we adopt a new optical character reader mechanism for character identification on number plates. We prove that the proposed hybrid approach is robust and works well on live object detection. The existing research focused on the prediction accuracy, which in most state-of-the-art methods (SOTA) is very similar. Thus, the precision among several use cases is also a good evaluation measure that was ignored in the existing research. It is evident that the performance of prediction systems suffers due to adverse weather conditions stated earlier. In such cases, the precision between events of detection may result in high variance that impacts the prediction of vehicles in unfavorable circumstances. The performance assessment of the proposed solution yields a precision of 98% on real-time data for Malaysian number plates, which can be generalized in the future to all sorts of vehicles around the globe.

Keywords

• security protocol
• automated data collection
• computer vision
• vehicle identification
• deep learning