Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi

IF 0.4 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
M. Abdurrahman, Haryadi Amran Darwito, Akuwan Saleh
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引用次数: 3

Abstract

In this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m.
基于树莓派的Haar级联和局部二值模式直方图人脸识别系统
在这个时代,车辆盗窃的发生已经成为一个相当频繁的问题,特别是在雅加达和泗水这样的大城市。虽然汽车安全技术非常先进(如无钥匙系统),但在很多情况下,小偷仍然可以闯入系统。一旦汽车被盗,汽车的下落不明,小偷逍遥法外。本研究的目的就是要克服这个问题。作为一种设备,本研究工作在与树莓派相机连接的树莓派3上。使用OpenCV库,用Haar级联法进行人脸检测,用局部二值模式直方图进行人脸识别。该设备必须连接到互联网,才能使用电报信息发送信息。研究结果表明,该设备系统在上午至下午的最佳光照强度为660 ~ 1000勒克斯,最佳识别距离为50厘米的情况下,成功实现了车主与偷车贼之间的人脸识别。获取室外条件下汽车位置的成功率为100%。即使有斜率或误差数据,也可以容忍,因为差异不是太高,约0.1-1.0 m。
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
EMITTER-International Journal of Engineering Technology
EMITTER-International Journal of Engineering Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
0.00%
发文量
7
审稿时长
12 weeks
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