A Detection System for Stolen Vehicles Using Vehicle Attributes With Deep Learning

Saini, P. K.; Bidhan, Karambir; Malhotra, Sona

doi:10.1109/ispcc48220.2019.8988389

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…ALPR refers to the process of recognizing the characters existing inside the vehicles' license plates through machine vision and artificial intelligence methods. Some of the most common applications in which the license plate recognition process is essential are traffic monitoring systems [7] [8] [9], navigation and vehicle tracking [10] [11], toll payments and control systems in public/private parking areas [12] [13], and the identification of stolen vehicles [14] [15]. In this regard, the correct extraction of characters from license plates is significantly essential to take further actions like enforcement and prosecution.…”

Section: Introductionmentioning

confidence: 99%

“…A typical ALPR system consists of three main modules to provide reasonable outcomes. These three steps are known as License Plate Detection (LPD), Character Segmentation (CS), and Optical Character Recognition (OCR) which can be found in most of the approaches [2] [3] [5] [7] [14] [15]. In the first step of the pipeline, i.e., LPD, the candidate regions containing vehicles' license plates are detected and cropped for further processing.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

et al. 2020

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The process of detecting vehicles' license plates, along with recognizing the characters inside them, has always been a challenging issue due to various conditions. These conditions include different weather and illumination, inevitable data acquisition noises, and some other challenging scenarios like the demand for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. This paper proposes a method for vehicle License Plates Detection (LPD) and Character Recognition (CR) as a unified application that presents significant accuracy and real-time performance. The mentioned system is designed for Iranian vehicle license plates, which have the characteristics of different resolution and layouts, scarce digits/characters, various background colors, and different font sizes. In this regard, the system uses a separate fine-tuned You Only Look Once (YOLO) version 3 platform for each of the mentioned phases and extracts Persian characters from input images in two automatic steps. For training and testing stages, a wide range of vehicle images in different challenging and straightforward conditions have been collected from practical systems installed as surveillance applications. Experimental results show an end-to-end accuracy of 95.05% on 5719 images. The test data included both color and grayscale images containing the vehicles with different distances and shooting angles with various brightness and resolution. Additionally, the system can perform the LPD and CR tasks in an average of 119.73 milliseconds for real life data, which illustrates a real-time performance for the system and usable applicability. The system is fully automated, and no pre-processing, calibration or configuration procedures are needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

et al. 2020

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Section: Introductionmentioning

confidence: 99%

“…In recent times, there has been a lot of research done on license plate recognition recently [2,3,4,5,6,7,8]. Saini et al [3] designed a deep learning based system where K-Nearest Neighbors algorithm and convolutional neural network classifier are used to identify the stolen/suspicious vehicles without human intervention. In the first stage, Google's Tensor Flow detection API is used to detect the vehicle with sub-modules to identify registration number and color from a real-time video stream.…”

Section: Introductionmentioning

confidence: 99%

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

Kadambari¹,

Nimmalapudi²

2020

Preprint

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Vehicle theft is arguably one of the fastestgrowing 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

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Vehicle Speed Estimation and Tracking Using Deep Learning and Computer Vision

Sathyabama

Devpura

Maroti

et al. 2022

Innovative Data Communication Technologies and Application

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A Detection System for Stolen Vehicles Using Vehicle Attributes With Deep Learning

Cited by 9 publications

References 7 publications

A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

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

Vehicle Speed Estimation and Tracking Using Deep Learning and Computer Vision

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