Localized anomaly detection via hierarchical integrated activity discovery
Date
2014
Authors
Chockalingam, Thiyagarajan, author
Rajopadhye, Sanjay, advisor
Anderson, Chuck, advisor
Pasricha, Sudeep, committee member
Bohm, Wim, committee member
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Volume Title
Abstract
With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, 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. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects.
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Subject
brain computing interfaces
Dirichlet prior
PLSA
PLSM
surveillance
topic modelling