Violent Behavioral Activity Classification Using Artificial Neural Network

R. Vrskova, R. Hudec, P. Sykora, P. Kamencay, M. Benco
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引用次数: 2

Abstract

Detection of video information is a great help in classifying non-standard / abnormal human behavior. It is more difficult to detect objects from videos when information in videos are time bound to each other. In this paper we discuss the need to detect and classify this data. Also, we try to improve classification process by various methods. A specially modified convolution neural network architecture was used along with Long Short-Term Memory (LSTM) and time distribution in experiment. Convolution neural layers for 2D data, in architecture were used.
基于人工神经网络的暴力行为分类
视频信息的检测对分类非标准/异常的人类行为有很大帮助。当视频中的信息彼此有时间约束时,从视频中检测物体会变得更加困难。本文讨论了对这些数据进行检测和分类的必要性。同时,我们尝试用各种方法来改进分类过程。实验中采用了一种特殊改进的卷积神经网络结构,结合了长短期记忆和时间分布。在架构中使用卷积神经层处理二维数据。
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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