Abnormal Event Detection in Videos using Spatiotemporal AutoEncoder

Abstract

The detection of abnormal events from surveillance video input is more difficult due to the small number of such occurrences that may occur over time. Through learning a supervised model, this can be achieved to detect such events from video sequences. The proposed system used Deep learning based Convolutional Long Short- Term Memory (Conv-LSTM) networks to learn video sequences as supervised models and to compute the regularity score from the learned frames. Any divergences on the computed scores represents the detection of events as the learning model has represented it through reconstruction of errors. The learned model can be chosen as the best model based on the detection performed through regularity scores. The learning videos of abnormal sequences are very less in real time video and which has to be determined through the model accurately. Thus, the proposed work is more challenging to get the event detection identification at a particular frame accurately. Auto encoder and decoders used in the proposed model. The model is also experimented on Avenue Dataset for detection of abnormal events. The Detection of Anomalous Occurrences from video sequences is accurately detected in experimental results. Key Words: Deep learning, Convolutional Neural Networks - Long Short-Term Memory (CNN-LSTM), Supervised learning, Regulatory scores