Real-time smart vehicle surveillance system
This addresses vehicle theft, a common unsolved crime, for law enforcement agencies, but it is incremental as it applies existing image processing and deep learning methods to this specific problem.
The paper tackles vehicle theft by proposing a real-time surveillance system that detects and tracks suspect vehicles from CCTV feeds, extracting attributes like make, model, color, and license plate number with minimal latency and accuracy loss.
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.