Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance
Unsupervised Real-Time Unusual Behavior Detection for Biometric-Assisted Visual Surveillance
复制标题
用于生物识别辅助视觉监控的无监督实时异常行为检测
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Y. Moon
中科院分区：
文献类型：
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作者：
Tsz;Y. Moon
本文提出了一种新颖的异常行为检测算法，用于实时获取生物特征数据以进行智能监控。我们的工作旨在设计一种完全无监督的方法来检测异常行为，而不使用任何显式的训练数据集。为此，所提出的方法从历史记录的行为中学习；这样异常行为的定义是根据以前的观察建模的，而不是手动标记的数据集。为了实现这一点，采用金字塔 Lucas-Kanade 算法来估计连续帧之间的光流，并将结果编码为流直方图。利用流直方图之间的相关性，可以通过应用主成分分析 (PCA) 来检测异常行为。该方法在室内和室外监控场景下进行评估。它显示出有希望的结果，我们的检测算法能够发现异常行为并自动适应行为模式的变化。
This paper presents a novel unusual behaviors detection algorithm to acquire biometric data for intelligent surveillance in real-time. Our work aims to design a completely unsupervised method for detecting unusual behaviors without using any explicit training dataset. To this end, the proposed approach learns from the behaviors recorded in the history; such that the definition of unusual behavior is modeled according to previous observations, but not a manually labeled dataset. To implement this, pyramidal Lucas-Kanade algorithm is employed to estimate the optical flow between consecutive frames, the results are encoded into flow histograms. Leveraging the correlations between the flow histograms, unusual actions can be detected by applying principal component analysis (PCA). This approach is evaluated under both indoor and outdoor surveillance scenarios. It shows promising results that our detection algorithm is able to discover unusual behaviors and adapt to changes in behavioral pattern automatically.
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DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
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作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA