Video Anomaly Detection using Convolutional Spatiotemporal Autoencoder

Abstract

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.