Hybrid Deep Network for Anomaly Detection
This work addresses the problem of detecting anomalies in surveillance videos for security applications, but it is incremental as it builds on existing auto-encoder and classification methods.
The paper tackles anomaly detection in surveillance videos by proposing a deep convolutional neural network that learns normal event characteristics and provides anomaly scores per frame, achieving competitive results on four benchmark datasets.
In this paper, we propose a deep convolutional neural network (CNN) for anomaly detection in surveillance videos. The model is adapted from a typical auto-encoder working on video patches under the perspective of sparse combination learning. Our CNN focuses on (unsupervisedly) learning common characteristics of normal events with the emphasis of their spatial locations (by supervised losses). To our knowledge, this is the first work that directly adapts the patch position as the target of a classification sub-network. The model is capable to provide a score of anomaly assessment for each video frame. Our experiments were performed on 4 benchmark datasets with various anomalous events and the obtained results were competitive with state-of-the-art studies.