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Robust and computationally efficient anomaly detection in videos is a problemin video surveillance systems. We propose a technique to increase robustnessand reduce computational complexity in a Convolutional Neural Network (CNN)based anomaly detector that utilizes the optical flow information of videodata. We reduce the complexity of the network by denoising the intermediatelayer outputs of the CNN and by using powers-of-two weights, which replaces thecomputationally expensive multiplication operations with bit-shift operations.Denoising operation during inference forces small valued intermediate layeroutputs to zero. The number of zeros in the network significantly increases asa result of denoising, we can implement the CNN about 10% faster than acomparable network while detecting all the anomalies in the testing set. Itturns out that denoising operation also provides robustness because thecontribution of small intermediate values to the final result is negligible.During training we also generate motion vector images by a GenerativeAdversarial Network (GAN) to improve the robustness of the overall system. Weexperimentally observe that the resulting system is robust to backgroundmotion.

Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks