Compression-based anomaly detection for video surveillance applications

• Main Author: Au, Carmen E.
• Format: Others
• Language: en
• Published: McGill University 2006
• Subjects: Computer Science.
• Online Access: http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98598

In light of increased demands for security, we propose a unique approach to automated video surveillance using anomaly detection. The success of this approach is dependent on the ability of the system to ascertain the novelty of a given image acquired by a video camera. We adopt a compression-based similarity measure to determine similarity between images in a video sequence. Images that are sufficiently similar to the previously-seen images are discarded; conversely, images that are sufficiently dissimilar are stored for comparison with future incoming images. === The use of a compression-based technique inherently reduces the heavy computational and storage demands that other video surveillance applications typically have placed on the system. In order to further reduce the computational and storage load, the anomaly detection algorithm is applied to edges and people, which are image features that have been extracted from the images acquired by the camera.