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Local distinguishability aggrandizing network for human anomaly detection.
Neural Networks ( IF 6.0 ) Pub Date : 2019-11-13 , DOI: 10.1016/j.neunet.2019.11.002
Maoguo Gong 1 , Huimin Zeng 1 , Yu Xie 1 , Hao Li 1 , Zedong Tang 1
Affiliation  

With the growing demand for an intelligent system to prevent abnormal events, many methods have been proposed to detect and locate anomalous behaviors in surveillance videos. However, most of these methods contain two shortcomings mainly: distraction of the network and insufficient discriminating ability. In this paper, we propose a local distinguishability aggrandizing network (LDA-Net) in a supervised manner, consisting of a human detection module and an anomaly detection module. In the human detection module, we obtain segmented patches of specific human subjects and take them as the input of the latter module to focus the network on learning motion characteristics of each person. In addition, considering that the auxiliary information, such as the specific type of an action, can aggrandize the whole network to extract distinguishable detail features of normal and abnormal behaviors, the proposed anomaly detection module comprises a primary binary classification sub-branch and an auxiliary distinguishability aggrandizing sub-branch, through which we can jointly detect anomalies and recognize actions. To further reduce the misclassification of the extremely imbalanced datasets, we design a novel inhibition loss function and embed it into the auxiliary sub-branch of the anomaly detection module. Experiments on several public benchmark datasets for frame-level and pixel-level anomaly detection show that the proposed supervised LDA-Net achieves state-of-the-art results on UCSD Ped2 and Subway Exit datasets.

中文翻译:

用于人类异常检测的本地可区分性强化网络。

随着对防止异常事件的智能系统的需求不断增长,已经提出了许多方法来检测和定位监视视频中的异常行为。但是,这些方法中的大多数主要存在两个缺点:网络分散注意力和区分能力不足。在本文中,我们提出了一种受监督的局部可区分性强化网络(LDA-Net),该网络由人类检测模块和异常检测模块组成。在人体检测模块中,我们获取特定人体主题的分段补丁,并将其作为后一个模块的输入,以使网络专注于学习每个人的运动特征。另外,考虑到辅助信息(例如动作的特定类型),为了增强整个网络以提取正常行为和异常行为的可区分细节特征,提出的异常检测模块包括一个主要的二进制分类子分支和一个辅助可区分性增强子分支,通过它们我们可以共同检测异常并识别动作。为了进一步减少极端不平衡的数据集的错误分类,我们设计了一种新颖的抑制损失函数,并将其嵌入到异常检测模块的辅助子分支中。在几个用于帧级和像素级异常检测的公共基准数据集上的实验表明,提出的受监督的LDA-Net在UCSD Ped2和Subway Exit数据集上达到了最新的结果。提出的异常检测模块包括一个主要的二进制分类子分支和一个辅助的可区分性强化子分支,通过它们我们可以共同检测异常并识别动作。为了进一步减少极端不平衡的数据集的错误分类,我们设计了一种新颖的抑制损失函数,并将其嵌入到异常检测模块的辅助子分支中。在几个用于帧级和像素级异常检测的公共基准数据集上的实验表明,提出的受监督的LDA-Net在UCSD Ped2和Subway Exit数据集上达到了最新的结果。提出的异常检测模块包括一个主要的二进制分类子分支和一个辅助的可区分性强化子分支,通过它们我们可以共同检测异常并识别动作。为了进一步减少极端不平衡的数据集的错误分类,我们设计了一种新颖的抑制损失函数,并将其嵌入到异常检测模块的辅助子分支中。在几个用于帧级和像素级异常检测的公共基准数据集上的实验表明,提出的受监督的LDA-Net在UCSD Ped2和Subway Exit数据集上达到了最新的结果。我们设计了一种新颖的抑制损失函数,并将其嵌入到异常检测模块的辅助子分支中。在几个用于帧级和像素级异常检测的公共基准数据集上的实验表明,提出的受监督的LDA-Net在UCSD Ped2和Subway Exit数据集上达到了最新的结果。我们设计了一种新颖的抑制损失函数,并将其嵌入到异常检测模块的辅助子分支中。在几个用于帧级和像素级异常检测的公共基准数据集上的实验表明,提出的受监督的LDA-Net在UCSD Ped2和Subway Exit数据集上达到了最新的结果。
更新日期:2019-11-13
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