Scenario Analysis in Videos
A scenario in videos is a set of objects (including people) interacting over space and time, e.g., a patient making a cup of tea at home, which is recognisable and meaningful. Video frames can be represented as object maps, which are spatially interrelated objects. Object maps change between frames. Scenario recognition in videos is to make sense of the sequences of object maps. It can be used for detecting normal and abnormal situations in applications such as assistive living, or more specifically, smart homes.
The predominant techniques for scenario recognition are probabilistic. Dynamic Bayesian Networks (DBN) is a probabilistic technique that has been widely used in computer vision for scenario recognition. Hidden Markov Models (HHM) is another popular probabilistic technique for scenario recognition. DBN and HMM are both numerical techniques, and training is required. It is difficult to have a training video archive that covers all appearances of a scenario, and the temporal variability is a challenge.
At the University of Ulster, extensive work has been conducted on sensor-based scenario/activity recognition for assistive living. In this project we plan to extend this line of work to a new arena that is based on videos – video-based scenario recognition. We plan to address the challenges in video-based scenario recognition by taking an ontology based approach augmented by DBN, HMM and the neighbourhood counting methodology.
- James M. REHG, Behavior Imaging: Using Computer Vision to Study Autism, Invited Speech at IAPR MVA2011 (http://www.mva-org.jp/mva2011/)
- J. Wu and J. M. Rehg. "CENTRIST: A Visual Descriptor for Scene Categorization." To appear in IEEE Trans. PAMI.
- H. Zhou, T. Hermans, A. Karandikar, and J. M. Rehg. "Movie Genre Classification via Scene Categorization." In Proc. ACM Multimedia 2010, Firenze, Italy.
- Ahmed Ziani and Cina Motamed. Temporal Bayesian Networks for Scenario Recognition. Lecture Notes in Computer Science, Volume 4522/2007, 689-698, 2007.
- Van-Thinh Vu, François Brémond and Monique Thonnat. Automatic Video Interpretation: A Recognition Algorithm for Temporal Scenarios Based on Pre-compiled Scenario Models. Lecture Notes in Computer Science, Volume 2626/2003, 523-533, 2003
- Van-Thinh VU, François BRÉMOND and Monique THONNAT. Automatic Video Interpretation: A Novel Algorithm for Temporal Scenario Recognition. The Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), 2003.
- Muralikrishna Sridhar, Anthony G. Cohn, David C. Hogg: Unsupervised Learning of Event Classes from Video. AAAI 2010
- Muralikrishna Sridhar, Anthony G. Cohn, David C. Hogg: Discovering an Event Taxonomy from Video using Qualitative Spatio-temporal Graphs. ECAI 2010: 1103-1104
- Hui Wang. "Nearest neighbors by neighborhood counting". IEEE Transactions on Pattern Analysis and Machine Intelligence". 28(6), 942-953, 2006.
First Supervisor: Wang, H Dr
Second Supervisor: Scotney, BW Prof
Third Supervisor: Liu, J Dr
Collaboration: This project does not involve collaboration with another establishment
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