Dynamic Hierarchical Dirichlet Process for Anomaly Detection in Video
This is a source code and synthetic data for dynamic hierarchical Dirichlet process for anomaly detection in video, introduced in O.Isupova, D.Kuzin, L.Mihaylova "Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video" in Proceedings of 19th International Conference of Information Fusion, Heidelberg, Germany, July 2016.
In this approach we consider the problem of anomaly detection as extracting typical motion patterns from data by topic modeling methods and detect video clips as abnormal if they have low values of likelihood computed with respect to these extracted typical motion patterns. Learning and inference is performed in a fully unsupervised manner.
If you use this code or model please cite the above mentioned paper.
EthicsThere is no personal data or any that requires ethical approval
PolicyThe data complies with the institution and funders' policies on access and sharing
Sharing and access restrictionsThe data can be shared openly
- The file formats are open or commonly used
Methodology, headings and units
- There is a readme.txt file describing the methodology, headings and units