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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaotao Zou Bhanu, B. |
| Copyright Year | 2008 |
| Description | Author affiliation: Center for Res. in Intell. Syst., Univ. of California, Riverside, CA (Xiaotao Zou; Bhanu, B.) |
| Abstract | Unlike existing methods that used the human actions or trajectories to analyze the human activity in overlapping field-of-views, this paper proposes the appearance and travel time-based human activity classification in the camera network of non-overlapping field-of-views. The mixture of Gaussian-based appearance similarity model incorporates the appearance variance between different cameras to address changes in varying lighting conditions. To address the problem of limited labeled training data, we propose the use of semi-supervised expectation-maximization algorithm for activity classification. The human activities observed in a simulated camera network with nine cameras and twenty-five nodes are classified into one normal and three anomalous classes. A similar camera network is built and tested in real-life experiments, in which the proposed approach achieves satisfactory performance. |
| Starting Page | 781 |
| Ending Page | 784 |
| File Size | 465638 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424417650 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2008.4711871 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Humans Surveillance Image sensors Intelligent networks Smart cameras Training data CMOS image sensors Charge-coupled image sensors Space exploration Intelligent systems semi-supervised learning surveillance activity analysis camera network |
| Content Type | Text |
| Resource Type | Article |
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