Convolutional Neural Network - Long Short Term Memory based IOT Node for Violence Detection

Nouar Aldahoul, H. A. Karim, Rishav Datta, Shreyash Gupta, Kashish Agrawal, Ahmad Albunni
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引用次数: 6

Abstract

Violence detection has been investigated extensively in the literature. Recently, IOT based violence video surveillance is an intelligent component integrated in security system of smart buildings. Violence video detector is a specific kind of detection models that should be highly accurate to increase the model's sensitivity and reduce the false alarm rate. This paper proposes a novel architecture of end-to-end CNN-LSTM (Convolutional Neural Network - Long Short-Term Memory) model that can run on low-cost Internet of Things (IOT) device such as raspberry pi board. The paper utilized CNN to learn spatial features from video's frames that were applied to LSTM for video classification into violence/non-violence classes. A complex dataset including two public datasets: RWF-2000 and RLVS-2000 was used for model training and evaluation. The challenging video content includes crowds and chaos, small object at far distance, low resolution, and transient action. Additionally, the videos were captured in various environments such as street, prison, and schools with several human actions such as eating, playing basketball, football, tennis, and swimming. The experimental results show good performance of the proposed violence detection model in terms of average metrics having an accuracy of 73.35 %, recall of 76.90 %, precision of 72.53 %, F1 score of 74.01 %, false negative rate of 23.10 %, false positive rate of 30.20 %, and AUC of 82.0 %. The proposed CNN-LSTM can balance good performance with low number of parameters and thus can be implemented on low-cost IOT node.
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卷积神经网络-基于长短期记忆的IOT节点暴力检测
暴力检测在文献中得到了广泛的研究。近年来,基于物联网的暴力视频监控已经成为智能建筑安防系统的一个智能组成部分。暴力视频探测器是一种特殊的检测模型,为了提高模型的灵敏度,降低虚警率,需要具有较高的准确率。本文提出了一种新颖的端到端CNN-LSTM(卷积神经网络-长短期记忆)模型架构,该模型可在树莓派板等低成本物联网设备上运行。本文利用CNN从视频帧中学习空间特征,并将其应用到LSTM中,将视频分类为暴力/非暴力类。采用RWF-2000和RLVS-2000两个公开数据集组成的复杂数据集进行模型训练和评估。具有挑战性的视频内容包括人群和混乱,远距离小物体,低分辨率和瞬态动作。此外,这些视频是在不同的环境中拍摄的,比如街道、监狱和学校,里面有一些人类的行为,比如吃东西、打篮球、踢足球、打网球和游泳。实验结果表明,该模型在平均指标方面表现良好,准确率为73.35%,召回率为76.90%,准确率为72.53%,F1分数为74.01%,假阴性率为23.10%,假阳性率为30.20%,AUC为82.0%。本文提出的CNN-LSTM可以在低成本的物联网节点上实现良好的性能和较少的参数。
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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