Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Thiyagarajan Chockalingam
M.S. Final
Dec 06, 2013, 3:00 pm
CS Conference Room
Localized Anomaly Detection Via Hierarchical Integrated Discovery
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 con-sider 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.
Adviser: Sanjay Rajopadhye
Co-Adviser: Chuck Anderson
Non-ECE Member: Anton Bohm
Member 3: Sudeep Pasricha
Addional Members:
[1] Thiyagarajan Chockalingam, Remi Emonet, Jean-Marc Odobez. “Localized Anomaly Detection via Hierarchical Integrated Activity Discovery.” 10-th edition of the IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2013
Program of Study: