论文标题
基于深度学习的交通监视系统缺失和可疑的汽车检测
Deep Learning Based Traffic Surveillance System For Missing and Suspicious Car Detection
论文作者
论文摘要
可以说，车辆盗窃是印度增长最快的犯罪类型之一。在某些城市地区，据信车辆盗窃案每天约为100。在这种不稳定的情况下，使用基于手动检查和射频识别（RFID）技术等传统方法，无法识别被盗的车辆。本文介绍了一个基于深度学习的自动交通监视系统，用于从闭路电视（CCTV）摄像机录像中检测偷来的汽车。它主要由四个部分组成：Select-detector，Image质量增强器，图像变压器和智能识别器。选择探测器用于提取包含车辆的框架，并以最小的时间复杂性有效地检测车牌。然后，使用图像质量增强器增强了车牌的质量，该图像质量增强器使用Pix2Pix生成的对抗网络（GAN）来增强受到时间变化（如低光，阴影等）影响的车牌。图像变形金刚用于解决对驾驶员识别效率低下的问题，这些牌照的效率低下，这些牌照识别不是水平的（侧角）转换了不同级别的转换和不同级别。 Smart识别器使用Tesseract光学角色识别（OCR）识别车牌号，并使用错误检测器纠正错误识别的字符。在政府的闭路电视摄像机录像上测试了拟议方法的有效性，这导致识别偷来的/可疑的汽车的精度为87％。
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%.
