Published on Fri Jul 17 2020

Deep Learning Based Traffic Surveillance System For Missing and Suspicious Car Detection

K. V. Kadambari, Vishnu Vardhan Nimmalapudi

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.

0
0
0
Abstract

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%.

Thu Dec 03 2020
Computer Vision
Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh
This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained. The license plate detection model is trained with mean average precision (mAP) of 90.50% and performed in a single TESLA T4 GPU.
0
0
0
Thu Oct 05 2017
Machine Learning
Real-Time Illegal Parking Detection System Based on Deep Learning
The increasing illegal parking has become more and more serious. Nowadays the method of detecting illegally parked vehicles is based on background segmentation. However, this method is weakly robust and sensitive to the environment. This paper proposes a novel illegal vehicle parking detection system.
0
0
0
Thu Aug 23 2018
Artificial Intelligence
Deep Learning Based Vehicle Make-Model Classification
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created afine-grained database using online vehicle marketplaces of Turkey.
0
0
0
Sun May 10 2020
Machine Learning
Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition
Vehicle tracking is an integral part of intelligent traffic management systems. The proposed system uses a novel approach to vehicle tracking using Vehicle License plate detection and recognition (VLPR) technique. Results were obtained at a speed of 30 frames per second with accuracy close to human.
0
0
0
Mon Apr 30 2018
Computer Vision
An Anti-fraud System for Car Insurance Claim Based on Visual Evidence
Insurance companies launch express vehicle insurance claim andsettlement by allowing customers uploading pictures taken by mobile devices. This kind of insurance claim is treated as small claim and can be processed manually or automatically in a quick fashion. But due to the increasing amount of claims every day, system or people are
0
0
0
Fri Oct 02 2020
Computer Vision
Artificial Intelligence Enabled Traffic Monitoring System
Deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles.
0
0
0