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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Figueira, D. Bazzani, L. Ha Quang Minh Cristani, M. Bernardino, A. Murino, V. |
| Copyright Year | 2013 |
| Description | Author affiliation: ISR - Inst. Super. Tecnico, Lisbon, Portugal (Figueira, D.; Bernardino, A.) || Pattern Anal. & Comput. Vision, Ist. Italiano di Tecnol., Genoa, Italy (Bazzani, L.; Ha Quang Minh; Cristani, M.; Murino, V.) |
| Abstract | Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary state-of-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem. |
| Starting Page | 111 |
| Ending Page | 116 |
| File Size | 443310 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479907038 |
| DOI | 10.1109/AVSS.2013.6636625 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-08-27 |
| Publisher Place | Poland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Histograms Optimized production technology Feature extraction Cameras Kernel Probes |
| Content Type | Text |
| Resource Type | Article |
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