Research on Privacy Protection Video Behavior Recognition Method Based on Improved SlowFast

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Yunxue Shao, Min Li, Lingfeng Wang
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引用次数: 0

Abstract

Surveillance cameras in public areas have had a positive impact on reducing violence, but have also raised concerns about privacy invasion. With these factors in mind, the Bullying10K neuromorphic dataset utilizes dynamic vision sensor (DVS) cameras to detect instances of violent behavior while protecting individual privacy. This dataset collects a variety of complex, fast violent actions from real-life scenarios as well as potentially occluded individuals. In this paper, for the characteristics of this dataset, the representative SlowFast neural network in behavior recognition is selected for research and improvement. First, a linear interpolation is applied to the Slow pathway to account for the absence of background influence in the Bullying10K dataset. Second, for the complex and fast action features and noise effects in the dataset, the interframe difference method is applied to the input of Fast pathway, which can effectively amplify the real dynamic signals and recognize the motion information in the video. Finally, it is difficult to prevent leakage of nonfacial information, such as gait data, against this dataset. The spatiotemporal attention fusion module (STAFM) is introduced, which not only better protects the privacy of nonfacial information but also improves the security and accuracy of the model when dealing with sensitive data, as well as enhances the generalization ability of the model. Experiments on Bullying10K show that the improved SlowFast exhibits significant advantages, including higher recognition accuracy, better protection of personal privacy, and better generalization capabilities. In addition, this paper is also validated on the UCF101 dataset, and the experimental results demonstrate the generalization of the improved method. The code of this paper is open-sourced at: https://github.com/MinL0128/STAFM-SlowFast.

基于改进SlowFast的隐私保护视频行为识别方法研究
公共场所的监控摄像头对减少暴力产生了积极影响,但也引起了人们对侵犯隐私的担忧。考虑到这些因素,欺凌10k神经形态数据集利用动态视觉传感器(DVS)相机来检测暴力行为的实例,同时保护个人隐私。这个数据集收集了各种复杂的、快速的暴力行为,这些行为来自现实生活场景以及潜在的闭塞个体。本文针对该数据集的特点,选取行为识别中具有代表性的SlowFast神经网络进行研究和改进。首先,将线性插值应用于慢路径,以解释在Bullying10K数据集中没有背景影响。其次,针对数据集中复杂快速的动作特征和噪声效应,将帧间差分方法应用于fast pathway的输入,可以有效放大真实动态信号,识别视频中的运动信息。最后,很难防止非面部信息(如步态数据)对该数据集的泄露。引入了时空注意力融合模块(STAFM),在更好地保护非面部信息隐私的同时,提高了模型在处理敏感数据时的安全性和准确性,增强了模型的泛化能力。在Bullying10K上的实验表明,改进后的SlowFast具有显著的优势,包括更高的识别精度、更好的个人隐私保护和更好的泛化能力。此外,本文还在UCF101数据集上进行了验证,实验结果证明了改进方法的泛化性。本文的代码是开源的:https://github.com/MinL0128/STAFM-SlowFast。
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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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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