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.