An infrared video detection and categorization system based on machine learning

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
David Švorc, Tomáš Tichý, M. Růžička
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引用次数: 5

Abstract

The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
基于机器学习的红外视频检测与分类系统
本文的主要目的是为不同类别的电动和传统(配备内燃机)车辆的检测和识别提供一种新的可能性。这些可能性是通过使用热摄像机和可视摄像机以及两种机器学习方法提供的。使用的方法是Haar级联分类器和卷积神经网络(CNN)。通过红外热像仪获得的热图像用于训练数据库。热像仪可以补充或替代视频摄像机和其他传统传感器的可见光谱,并提供车辆类型识别所需的详细识别和分类数据。第一种方法被用作目标检测器,用于定位道路上的车辆,而无需进一步分类。第二种方法在热图像数据库上进行车辆识别训练,并根据定义的类别之一对定位的目标进行分类。结果证实,根据车辆外部热特征,结合机器学习方法进行车辆类型识别,利用红外热成像技术进行车辆驾驶分类是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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