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Content Provider | IEEE Xplore Digital Library |
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Author | Turaga, P.K. Veeraraghavan, A. Chellappa, R. |
Copyright Year | 2007 |
Description | Author affiliation: Maryland Univ., College Park (Turaga, P.K.; Veeraraghavan, A.; Chellappa, R.) |
Abstract | Clustering video sequences in order to infer and extract activities from a single video stream is an extremely important problem and has significant potential in video indexing, surveillance, activity discovery and event recognition. Clustering a video sequence into activities requires one to simultaneously recognize activity boundaries (activity consistent subsequences) and cluster these activity subsequences. In order to do this, we build a generative model for activities (in video) using a cascade of dynamical systems and show that this model is able to capture and represent a diverse class of activities. We then derive algorithms to learn the model parameters from a video stream and also show how a single video sequence may be clustered into different clusters where each cluster represents an activity. We also propose a novel technique to build affine, view, rate invariance of the activity into the distance metric for clustering. Experiments show that the clusters found by the algorithm correspond to semantically meaningful activities. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 421617 |
Page Count | 8 |
File Format | |
ISBN | 1424411793 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2007.383170 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-06-17 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Video sequences Streaming media Clustering algorithms Automation Educational institutions Indexing Surveillance Airports Pattern analysis Hidden Markov models |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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