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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Karanam, Srikrishna Li, Yang Radke, Richard J. |
| Copyright Year | 2015 |
| Abstract | This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view. Unlike previous approaches that learn Mahalanobis-like distance metrics in some transformed feature space, we propose to learn a dictionary that is capable of discriminatively and sparsely encoding features representing different people. Our approach directly addresses two key challenges in person re-identification: viewpoint variations and discriminability. First, to tackle viewpoint and associated appearance changes, we learn a single dictionary to represent both gallery and probe images in the training phase. We then discriminatively train the dictionary by enforcing explicit constraints on the associated sparse representations of the feature vectors. In the testing phase, we re-identify a probe image by simply determining the gallery image that has the closest sparse representation to that of the probe image in the Euclidean sense. Extensive performance evaluations on three publicly available multi-shot re-identification datasets demonstrate the advantages of our algorithm over several state-of-the-art dictionary learning, temporal sequence matching, and spatial appearance and metric learning based techniques. |
| Starting Page | 4516 |
| Ending Page | 4524 |
| File Size | 424096 |
| Page Count | 9 |
| File Format | |
| ISSN | 23807504 |
| e-ISBN | 9781467383912 |
| DOI | 10.1109/ICCV.2015.513 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-12-07 |
| Publisher Place | Chile |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Dictionaries Cameras Probes Measurement Optimization Feature extraction Encoding |
| Content Type | Text |
| Resource Type | Article |
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