One-Shot Learning for Surveillance Anomaly Recognition using Siamese 3D CNN

One-shot image recognition has been explored for many applications in computer vision community. However, its applications in video analytics is not deeply investigated yet. For instance, surveillance anomaly recognition is an open challenging problem and one of its hurdles is the lack of accurate temporally annotated data. This paper addresses the lack of data issue using one-shot learning strategy and proposes an anomaly recognition framework which exploits a 3D CNN siamese network that yields the similarity between two anomaly sequences. This paper also investigates the existing 3D CNNs for this task and then proposes a lightweight 3D CNN model that efficiently handles one-shot anomaly recognition. Once our network is trained, then we can use the powerful discriminative 3D CNN features to predict anomalies not only for the new data but also for entirely new classes. The proposed model is trained using temporally annotated test set of UCF Crime dataset. Finally, the trained model is used to recognize the anomalies and produce temporal automatic labels for the video level weakly annotated training set of the dataset.