Real Time Crime Detection Using Deep Learning Algorithm

P. Sivakumar, Jayabalaguru. V, R. R, Kalaisriram. S
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引用次数: 7

Abstract

The Crime Rate and number of Criminals are increasing day by day, so there is great concern about the security issues, so to prevent and identify the crime before it occurs is the primary goal of the police officials. With the help of Recent Technologies, especially CCTV is normally deployed in every private and public area to control crime but it needs human supervision to monitor. It's hard for a human to monitor many screens at the same time. It leads to many errors. To overcome these problems, we proposed Real-Time Crime Detection Technique using a Deep Learning Algorithm which monitors real-time videos and alerts the nearby Cybercrime admin about the occurrence of crime with current location. In this paper, We present YOLO as our object detection algorithm. Our architecture is extremely fast and process image in real-time at 45 frames per second.
使用深度学习算法的实时犯罪检测
犯罪率和犯罪分子的数量日益增加,因此人们对安全问题非常关注,因此在犯罪发生之前预防和识别犯罪是警察的首要目标。在现代科技的帮助下,特别是闭路电视通常部署在每个私人和公共区域,以控制犯罪,但需要人工监控。一个人很难同时监控多个屏幕。它会导致许多错误。为了克服这些问题,我们提出了一种使用深度学习算法的实时犯罪检测技术,该技术可以监控实时视频,并向附近的网络犯罪管理员发出当前位置发生犯罪的警报。在本文中,我们提出YOLO作为我们的目标检测算法。我们的架构非常快,可以以每秒45帧的速度实时处理图像。
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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