基于深度卷积神经网络的人群管理与监控

Pratiksha Singh, A. K. Daniel
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引用次数: 0

摘要

印度被认为是世界上人口最多的国家之一。由于人口的增加,犯罪活动在拥挤的地方更加频繁,因此犯罪也在增加。拥挤也面临着很多疾病。因此,人群管理和监控是非常重要的,因此从安全的角度来看,人群管理和监控在识别人群中群体/个人的行为中起着非常重要的作用,利用视频和图像序列进行人员计数并检测这些不良行为元素,本文提出了一种以人群管理和监控人员计数为对象的检测技术模型。本文提出了深度卷积神经网络和支持向量机。数据集取自Mall, Kumbh Mela和UCFD。通过对数据的训练和测试,提高了模型的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crowd Management and Monitoring using Deep Convolutinal Neural Network
India is counted as one of the most populated countries in the world. A lot of crime is also increasing due to the increasing population, as criminal activities are more frequent in a crowded place. Being crowded is also facing a lot of diseases. Therefore, crowd management and monitoring are very important therefore viewed from the security, crowd management, and monitoring plays a very important role in identifying group/ individual’s behavior in a crowd using video and image sequence for counting the person and detection of such misbehavior elements, This paper proposed a model for crowd management and monitoring person counting as object detection techniques. This paper proposed Deep Convolutional Neural Network, and Support Vector Machine. The data set are taken from Mall, Kumbh Mela, and UCFD. The performance of the model using training and testing of data is improved.
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