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| Content Provider | Springer Nature Link |
|---|---|
| Author | Oliver, Javier Albiol, Alberto Albiol, Antonio Mossi, José M. |
| Copyright Year | 2015 |
| Abstract | This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use of latent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality. Latent features can also cope with missing data in case of occlusions. Different probabilistic latent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of the latent features significantly improves the re-identification rates compared to state-of-the-art works. |
| Starting Page | 549 |
| Ending Page | 561 |
| Page Count | 13 |
| File Format | |
| ISSN | 14337541 |
| Journal | Pattern Analysis & Applications |
| Volume Number | 19 |
| Issue Number | 2 |
| e-ISSN | 1433755X |
| Language | English |
| Publisher | Springer London |
| Publisher Date | 2015-06-20 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Bodyprint Probabilistic PCA Factor analysis Missing data Re-identification Surveillance Person detection Appearance matching Kinect Pattern Recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition |
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