论文标题
视频中的视觉异常检测由变量自动编码器
Visual anomaly detection in video by variational autoencoder
论文作者
论文摘要
视频异常检测是异常检测和视觉智能的交集。它在监视，安全性，自动驾驶汽车和作物监测中具有商业应用。视频可以捕获各种异常。由于需要标记培训数据所需的努力，因此无监督的训练视频检测模型的方法更实用，是一个自动编码器是一种神经网络，该神经网络经过训练，可以使用输入的潜在表示，也称为瓶颈层。与潜在矢量作为瓶颈层相比，变分自动编码器使用分布（平均值和方差），并且可以具有更好的正则化效果。在本文中，我们展示了卷积LSTM与变化卷积LSTM自动编码器的性能之间的比较
Video anomalies detection is the intersection of anomaly detection and visual intelligence. It has commercial applications in surveillance, security, self-driving cars and crop monitoring. Videos can capture a variety of anomalies. Due to efforts needed to label training data, unsupervised approaches to train anomaly detection models for videos is more practical An autoencoder is a neural network that is trained to recreate its input using latent representation of input also called a bottleneck layer. Variational autoencoder uses distribution (mean and variance) as compared to latent vector as bottleneck layer and can have better regularization effect. In this paper we have demonstrated comparison between performance of convolutional LSTM versus a variation convolutional LSTM autoencoder
