论文标题
使用车牌检测和识别的基于深度学习的车辆跟踪系统
Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition
论文作者
论文摘要
车辆跟踪是智能交通管理系统不可或缺的一部分。以前的车辆跟踪实现了使用的全球定位系统（GPS）的系统，该系统在其智能手机上提供了个人车辆的位置。拟议的系统使用新颖的方法使用车辆牌照板检测和识别（VLPR）技术来进行车辆跟踪，该技术可以与交通管理系统大规模集成。实施VLPR的初始方法使用了相当实验性和启发式的简单图像处理技术。随着深度学习和计算机视觉的发作，人们可以创建强大的VLPR系统，从而产生接近人类效率的结果。以前的实现基于深度学习，利用对象检测和支持向量机进行检测以及基于启发式图像处理的识别方法。所提出的系统使用场景文本检测模型架构用于车牌检测，并为了识别它使用光学角色识别引擎（OCR）Tesseract。当使用NVIDIA GE-FORCE RTX 2080TI GPU在高速公路视频上进行测试时，该系统获得了非凡的结果，以每秒30帧的速度获得结果，其准确性接近人类。
Vehicle tracking is an integral part of intelligent traffic management systems. Previous implementations of vehicle tracking used Global Positioning System(GPS) based systems that gave location of the vehicle of an individual on their smartphones.The proposed system uses a novel approach to vehicle tracking using Vehicle License plate detection and recognition (VLPR) technique, which can be integrated on a large scale with traffic management systems. Initial methods of implementing VLPR used simple image processing techniques which were quite experimental and heuristic. With the onset of Deep learning and Computer Vision, one can create robust VLPR systems that can produce results close to human efficiency. Previous implementations, based on deep learning, made use of object detection and support vector machines for detection and a heuristic image processing based approach for recognition. The proposed system makes use of scene text detection model architecture for License plate detection and for recognition it uses the Optical character recognition engine (OCR) Tesseract. The proposed system obtained extraordinary results when it was tested on a highway video using NVIDIA Ge-force RTX 2080ti GPU, results were obtained at a speed of 30 frames per second with accuracy close to human.
