Unsupervised Anomaly Detection from Time-of-Flight Depth Images
This work addresses video anomaly detection for surveillance or monitoring applications, but it is incremental as it adapts existing methods to a new data modality.
The paper tackles the problem of video anomaly detection by using depth images instead of traditional monochrome or RGB data, proposing an autoencoder-based method with a modified loss function that leverages depth-derived foreground masks to improve performance, achieving results on a large public dataset where no prior results existed.
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.