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首页> 外文会议>International conference on omputational science and technology >A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network
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A Robust Abnormal Behavior Detection Method Using Convolutional Neural Network

机译：一种稳健的异常行为检测方法，卷积神经网络

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摘要

A behavior is considered abnormal when it is seen as unusual under certain contexts. The definition for abnormal behavior varies depending on situations. For example, people running in a field is considered normal but is deemed abnormal if it takes place in a mall. Similarly, loitering in the alleys, fighting or pushing each other in public areas are considered abnormal under specific circumstances. Abnormal behavior detection is crucial due to the increasing crime rate in the society. If an abnormal behavior can be detected earlier, tragedies can be avoided. In recent years, deep learning has been widely applied in the computer vision field and has acquired great success for human detection. In particular, Convolutional Neural Network (CNN) has shown to have achieved state-of-the-art performance in human detection. In this paper, a CNN-based abnormal behavior detection method is presented. The proposed approach automatically learns the most discriminative characteristics pertaining to human behavior from a large pool of videos containing normal and abnormal behaviors. Since the interpretation for abnormal behavior varies across contexts, extensive experiments have been carried out to assess various conditions and scopes including crowd and single person behavior detection and recognition. The proposed method represents an end-to-end solution to deal with abnormal behavior under different conditions including variations in background, number of subjects (individual, two persons or crowd), and a range of diverse unusual human activities. Experiments on five benchmark datasets validate the performance of the proposed approach.
机译：当在某些情况下被视为异常时，行为被认为是异常的。异常行为的定义根据情况而异。例如，在字段中运行的人被认为是正常的，但如果在商场中发生时被视为异常。同样，在公共区域中的小巷中互相战斗或推动在特定情况下被认为是异常的。由于社会的犯罪率增加，异常行为检测至关重要。如果之前可以检测到异常行为，则可以避免悲剧。近年来，深入学习已广泛应用于计算机视野领域，并获得了人类检测的巨大成功。特别地，卷积神经网络（CNN）已显示在人类检测中实现了最先进的性能。本文提出了一种基于CNN的异常行为检测方法。拟议的方法自动学习与含有正常和异常行为的大型视频中有关人类行为的最辨别特征。由于对异常行为的解释因上下文而异，因此已经进行了广泛的实验，以评估包括人群和单人行为检测和识别的各种条件和范围。该方法代表了一种端到端解决方案，用于处理不同条件下的异常行为，包括背景，受试者数量（个人，两个人或人群）以及一系列不同的不寻常的人类活动。五个基准数据集的实验验证了所提出的方法的性能。

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