Vehicle theft is arguably one of the fastest-growing types of crime in India. Identification of stolen vehicles in such precarious scenarios is not currently possible using traditional methods. This paper presents a deep learning-based automatic traffic surveillance system for the detection of stolen/suspicious cars from closed circuit television(CCTV) camera footage.
Vehicle theft is arguably one of the fastest-growing types of crime in India.
In some of the urban areas, vehicle theft cases are believed to be around 100
each day. Identification of stolen vehicles in such precarious scenarios is not
possible using traditional methods like manual checking and radio frequency
identification(RFID) based technologies. This paper presents a deep learning
based automatic traffic surveillance system for the detection of
stolen/suspicious cars from the closed circuit television(CCTV) camera footage.
It mainly comprises of four parts: Select-Detector, Image Quality Enhancer,
Image Transformer, and Smart Recognizer. The Select-Detector is used for
extracting the frames containing vehicles and to detect the license plates much
efficiently with minimum time complexity. The quality of the license plates is
then enhanced using Image Quality Enhancer which uses pix2pix generative
adversarial network(GAN) for enhancing the license plates that are affected by
temporal changes like low light, shadow, etc. Image Transformer is used to
tackle the problem of inefficient recognition of license plates which are not
horizontal(which are at an angle) by transforming the license plate to
different levels of rotation and cropping. Smart Recognizer recognizes the
license plate number using Tesseract optical character recognition(OCR) and
corrects the wrongly recognized characters using Error-Detector. The
effectiveness of the proposed approach is tested on the government's CCTV
camera footage, which resulted in identifying the stolen/suspicious cars with
an accuracy of 87%.