Anomaly Detection in Surveillance Videos by Future Appearance-motion Prediction

Anomaly detection in surveillance videos is the identification of rare events which produce different features from normal events. In this paper, we present a survey about the progress of anomaly detection techniques and introduce our proposed framework to tackle this very challenging objective. Our approach is based on the more recent state-of-the-art techniques and casts anomalous events as unexpected events in future frames. Our framework is so flexible that you can replace almost important modules by existing state-of-the-art methods. The most popular solutions only use future predicted informations as constraints for training a convolutional encode-decode network to reconstruct frames and take the score of the difference between both original and reconstructed information. We propose a fully future prediction based framework that directly defines the feature as the difference between both future predictions and ground truth informations. This feature can be fed into various types of learning model to assign anomaly label. We present our experimental plan and argue that our framework's performance will be competitive with state-of-the art scores by presenting early promising results in feature extraction.