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We present a novel unsupervised deep learning framework for anomalous eventdetection in complex video scenes. While most existing works merely usehand-crafted appearance and motion features, we propose Appearance and MotionDeepNet (AMDN) which utilizes deep neural networks to automatically learnfeature representations. To exploit the complementary information of bothappearance and motion patterns, we introduce a novel double fusion framework,combining both the benefits of traditional early fusion and late fusionstrategies. Specifically, stacked denoising autoencoders are proposed toseparately learn both appearance and motion features as well as a jointrepresentation (early fusion). Based on the learned representations, multipleone-class SVM models are used to predict the anomaly scores of each input,which are then integrated with a late fusion strategy for final anomalydetection. We evaluate the proposed method on two publicly available videosurveillance datasets, showing competitive performance with respect to state ofthe art approaches.

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection