Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
It addresses the problem of identifying abnormal events in video surveillance for security applications, but it is incremental as it builds on existing autoencoder and benchmark approaches.
The paper tackles anomaly detection in videos, including crowded scenes, by proposing a spatiotemporal autoencoder that achieves detection accuracy comparable to state-of-the-art methods with speeds up to 140 fps.
We present an efficient method for detecting anomalies in videos. Recent applications of convolutional neural networks have shown promises of convolutional layers for object detection and recognition, especially in images. However, convolutional neural networks are supervised and require labels as learning signals. We propose a spatiotemporal architecture for anomaly detection in videos including crowded scenes. Our architecture includes two main components, one for spatial feature representation, and one for learning the temporal evolution of the spatial features. Experimental results on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of our method is comparable to state-of-the-art methods at a considerable speed of up to 140 fps.