利用神经网络提取红外图像的时空人群特征

3区 计算机科学 Q1 Computer Science
Anas M. Al-Oraiqat, Oleksandr Drieiev, Hanna Drieieva, Yelyzaveta Meleshko, Hazim AlRawashdeh, Karim A. Al-Oraiqat, Yassin M. Y. Hasan, Noor Maricar, Sheroz Khan
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引用次数: 0

摘要

人群可能导致严重的灾难后果,造成人员伤亡。基于人工智能的人群分析系统可以处理通过公共摄像头或无人机拍摄的视频。作为过去几年的热门研究领域,该系统的目标不仅是识别人群的存在,还要预测人群形成的概率,以便及时发出警告和采取预防措施。这些系统将大大降低潜在灾害的发生概率。开发有效的系统是一项具有挑战性的任务,特别是由于自然发生的各种条件、人或背景像素区域的变化、噪声、个人行为、人群移动的相对数量/分布/方向以及人群聚集的原因等因素。本文提出了一种基于 U-Net 卷积神经网络的红外视频处理系统,用于红外视频帧中的人群监测,以帮助估计具有正常或异常趋势的人群。所提出的 U-Net 架构旨在高效提取人群特征,实现足够的人群标记精度,并在滤波器深度和数量方面与最佳网络配置竞争,从而最大限度地减少系数数量。为了进一步加快处理速度、节省硬件资源/实施面积和降低功耗,所测量的优化网络系数以卡诺尼-有符号数字表示,非零(± 1)位数最少,从而最大限度地减少了所有乘法器的底层移位-加法/减法运算次数。计算成本的大幅降低使所提出的 U-Net 能够有效适用于资源受限的低功耗应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spatiotemporal crowds features extraction of infrared images using neural network

Spatiotemporal crowds features extraction of infrared images using neural network

Crowds can lead up to severe disasterous consequences resulting in fatalities. Videos obtained through public cameras or captured by drones flying overhead can be processed with artificial intelligence-based crowd analysis systems. Being a hot area of research over the past few years, the goal is not only to identify the presence of crowds but also to predict the probability of crowd-formation in order to issue timely warnings and preventive measures. Such systems will significantly reduce the probablity of the potential disasters. Developing effective systems is a challenging task, especially due to factors such as naturally occuring diverse conditions, variations in people or background pixel areas, noise, behaviors of individuals, relative amounts/distributions/directions of crowd movements, and crowd building reasons. This paper proposes an infrared video processing system based on U-Net convolutional neural network for crowd monitoring in infrared video frames to help estimate the people crowd with normal or abnormal trends. The proposed U-Net architecture aims to efficiently extract crowd features, achieve sufficient people marking-up accuracy, competitively with optimal network configurations in terms of the depth and number of filters to consequently minimise the number of coefficients. For further faster processing, hardware resources/implementation area savings, and lower power, the optimized network coefficients measured are represented in Canonic-Signed Digit with minimal number of nonzero (± 1) digits, minimizing the number of underlying shift-add/subtract operations of all multipliers. The achieved significantly reduced computational cost makes the proposed U-Net effectively suitable for resource-constrained and low power applications.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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