自编码
计算机科学
编码器
人工智能
卷积神经网络
异常检测
模式识别(心理学)
卷积码
试验装置
异常(物理)
乙状窦函数
解码方法
计算机视觉
算法
深度学习
人工神经网络
操作系统
物理
凝聚态物理
作者
Rashmiranjan Nayak,Umesh Chandra Pati,Santos Kumar Das
标识
DOI:10.1109/ic3a48958.2020.233292
摘要
A convolutional spatiotemporal autoencoder is used for video anomaly detection. The proposed model architecture comprises of three major sections, such as spatial encoder, temporal encoder-decoder, and spatial decoder. The spatial encoder is implemented using three layers of the convolutional layers. Then, the temporal encoder-decoder is realized with the help of Convolutional Long Short Term Memory (ConvLSTM), gated with the tanh and sigmoid activation functions. Finally, the spatial decoder is implemented using three layers of deconvolutional layers. The proposed model is trained only on the dataset comprises the normal classes by minimizing the reconstruction error. Later, when the trained model is tested using the test dataset susceptible to contain anomalous activities, then high reconstruction error has resulted. Subsequently, a high anomaly score and low regularity score has resulted. When the regularity score of the frames falls below the set threshold level, then the corresponding frames are treated as anomalous ones. The proposed model is trained and tested on UCSD Ped1 and Ped2 dataset successfully. The results of the performance evaluation are found to be promising.
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