Real-time smart vehicle surveillance system

TL;DR

This paper presents a real-time vehicle surveillance system that employs image processing and deep learning to detect, track, and identify vehicles from CCTV footage, aiding law enforcement in reducing vehicle theft.

Contribution

It introduces a comprehensive system combining multiple vehicle attributes extraction with real-time processing capabilities for law enforcement applications.

Findings

System achieves real-time detection with minimal latency

High accuracy in vehicle attribute extraction

Effective in law enforcement scenarios

Abstract

Over the last decade, there has been a spike in criminal activity all around the globe. According to the Indian police department, vehicle theft is one of the least solved offenses, and almost 19% of all recorded cases are related to motor vehicle theft. To overcome these adversaries, we propose a real-time vehicle surveillance system, which detects and tracks the suspect vehicle using the CCTV video feed. The proposed system extracts various attributes of the vehicle such as Make, Model, Color, License plate number, and type of the license plate. Various image processing and deep learning algorithms are employed to meet the objectives of the proposed system. The extracted features can be used as evidence to report violations of law. Although the system uses more parameters, it is still able to make real time predictions with minimal latency and accuracy loss.