Publications

Abstract : Modeling abnormal spatiotemporal events is challenging since data belonging to abnormal activities are less in the course of a surveillance stream. We solve this issue using a normality modeling approach, where abnormalities are detected as deviations from the normal patterns. To this end, we propose a residual spatiotemporal autoencoder, which is trainable end-to-end to carry out the anomaly detection task in surveillance videos. Irregularities are detected using the reconstruction loss, where normal frames are reconstructed well with a low reconstruction cost, and the converse is identified as abnormal frames. We evaluate the effect of residual connections in the STAE architecture and presented good practices to train an autoencoder for video anomaly detection using benchmark datasets, namely CUHK-Avenue, UCSD-Ped2, and Live Videos. Comparisons with the existing approaches prove that the effectiveness of residual blocks is incremental than going deeper with additional layers to train a spatiotemporal autoencoder with good generalization across datasets.