Anomaly Detection Based On Convolutional Autoencoder

Anomaly detection is an import domain in computer vision.It aims at detecting anomaly behaviors in videos of crowded scenes.With the development of economics and increasement on population mobility,there is an increasing requirement of secu-rity guards in many public occasions,such as subway entrances and market halls.As a result,automatic anomaly detection technologies have great commercial value in se-curity.Recently,deep learning espeicially convolutional neural network has achieved great advancement in some import tasks in computer vision such as object detection,im-age segmentation,etc.This thesis proposed two approaches based on the convolutional autoencoder to solve the task of anomaly detection in crowded scenes:1.Autoencoder can be trained on data unsupervisely and get sparse features of data by optimizing construction loss.It will output low construction loss with normal data and output high construction loss with abnormal data.We can use this property to detect anomaly frames.We proposed a method which reconstructs the input and predicts future frames at the same time and achieve good results in experiment2.The features in the latent layer of autoencoder are sparse representations of the input data.It can be used to classify scenes.Our method uses an autoencoder to en-code both spatial and temoral information in videos and detect anomaly by using one-class SVM.This method achieves increasement both on efficiency and preci-sion.