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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Zhou, Yue Xu, Wei Tao, Hai Gong, Yihong |
| Copyright Year | 2005 |
| Description | Author affiliation: University of California, Santa Cruz (Tao, Hai) || NEC Laboratories America, Inc. (Xu, Wei; Gong, Yihong) || University of Illinois at Urbana- Champaign (Zhou, Yue) |
| Abstract | Robust and accurate background segmentation is crucial for surveillance applications and is a key element in visual tracking, layer-based compression, and silhouette-based 3D reconstruction. In this paper, we present a novel spatial-temporal model that describes the appearance and dynamics of background scenes at multiple resolutions. We propose a time-dependent Markov Random Field (MRF) to represent the state of foreground and background at each pixel in the spatial-temporal pyramid. Pixels are linked spatially and temporally across frames. The probability of adding/deleting a foreground object is calculated by online learning algorithm and is used as prior information in computing foreground label. We use Gibbs Sampling to solve the MRF in a Maximum A Posterior (MAP) framework. Experimental results show that this real-time algorithm is able to segment the foreground object accurately from videos and more resilient to distractions such as imaging noise, illumination changes, camera shakes, and random motion in the scene. |
| Starting Page | 8 |
| Ending Page | 13 |
| File Size | 257167 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769522718 |
| DOI | 10.1109/ACVMOT.2005.32 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-01-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Layout Robustness Surveillance Image reconstruction Spatial resolution Markov random fields Probability Sampling methods Image segmentation Videos |
| Content Type | Text |
| Resource Type | Article |
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