Name
Abstract | ||
|---|---|---|
With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this article, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/AVSS.2013.6636615 | Advanced Video and Signal Based Surveillance |
Keywords | Field | DocType |
cameras,probability,video signal processing,camera installations,hierarchical integrated activity discovery,hierarchical multicamera processing,localized anomaly detection,temporal probabilistic models,videos | Iterative reconstruction,Anomaly detection,Computer vision,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Probabilistic latent semantic analysis,Probabilistic logic,Dirichlet distribution,Topic model | Conference |
Citations | PageRank | References |
4 | 0.37 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thiyagarajan Chockalingam | 1 | 4 | 0.37 |
| Remi Emonet | 2 | 103 | 8.49 |
| Jean-Marc Odobez | 3 | 140 | 19.50 |