利用闭路电视监控实现智能快速交通的大数据

I. A. Dahlan, F. Hamami, S. Supangkat, F. Hidayat
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引用次数: 4

摘要

本文介绍了一种基于闭路电视监控的快速交通智能系统的实现方法。研究者提出了深度学习算法,利用卷积神经网络(CNN)检测物体,监控乘客行为,如流量分析、避开危险区域、防止入侵者[1][2]。本文的研究也在万隆火车站多CCTV源的场景中进行。该系统旨在使车站更好,能够在多个范围领域(安全、可靠、方便)提高服务质量[3]。本课题的研究目的是在智能站点中实现CCTV智能监控。该系统由深度学习算法和Hadoop、Apache Kafka、Apache Spark等大数据技术组成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data Implementation of Smart Rapid Transit using CCTV Surveillance
This paper presents the implementation on smart system for rapid transit using CCTV surveillance. Researchers proposed deep learning algorithms to detect objects with Convolutional Neural Network (CNN) and monitoring passengers’ behavior like flow analytics, avoiding dangerous areas, and preventing intruder visitor[1][2].Research of this paper also implemented in Railway Station in Bandung with multiple CCTV sources. The system aims to make station better and able to improve quality of service in many scope areas (safe, secure and convenient)[3].Objective of this research is to develop smart surveillance with CCTV in smart station. The system consists of deep learning algorithm and big data technologies such as Hadoop, Apache Kafka and Apache Spark.
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