Unsupervised Learning Approach for Abnormal Event Detection in Surveillance Video by Hybrid Autoencoder

Abnormal detection plays an important role in video surveillance. LSTM encoder–decoder is used to learn representation of video sequences and applied for detecting abnormal event in complex environment. The learned representation of LSTM encoder–decoder is learned from encoder, and it is crucial for decoder. However, LSTM encoder–decoder generally fails to account for the global context of the learned representation with a fixed dimension representation. In this paper, we explore a hybrid autoencoder architecture, which not only extracts better spatio-temporal context, but also improves the extrapolate capability of the corresponding decoder by the shortcut connection. The experiment shows that the hybrid model performs better than the state-of-the-art anomaly detection methods in both qualitative and quantitative ways on benchmark datasets.