A fast and accurate video anomaly detection and localisation method is presented. The speed and localisation accuracy are two ongoing challenges in real-world anomaly detection. We introduce two novel cubic-patch-based anomaly detector where one works based on power of an auto-encoder (AE) on reconstituting an input video patch and another one is based on the power of sparse representation of an input video patch. It is found that if an AE is efficiently trained on all normal patches, the anomaly patch in testing phase has a more reconstruction error than a normal patch. Also if a sparse AE is learned based on normal training patches, we expect that the given patch to AE is represented sparsely. If the representation is not enough sparse it is considered as a good candidate to be anomaly. For being more fast, these two detectors are combined as a cascade classifier. First, all small patches on test video frame are scanned, those which have not enough sparse representation are resized and sent to next detector for more careful evaluation. The experiment results show that the method mentioned here has a better performance especially in run-time measure than state-of-the-art methods on two UMN and UCSD benchmarks.