Name
Abstract | ||
|---|---|---|
Deep learning methods show strong ability in extracting high-level features for images in the field of person re-identification. The produced features help inherently distinguish pedestrian identities in images. However, on deep learning models over-fitting and discriminative ability of the learnt features are still challenges for person re-identification. To alleviate model over-fitting and further enhance the discriminative ability of the learnt features, we propose siamese pedestrian alignment networks (SPAN) for person re-identification. SPAN employs two streams of PAN (pedestrian alignment networks) to increase the size of network inputs over limited training samples and effectively alleviate network over-fitting in learning. In addition, a verification loss is constructed between the two PANs to adjust the relative distance of two input pedestrians of the same or different identities in the learned feature space. Experimental verification is conducted on six large person re-identification data sets and the experimental results demonstrate the effectiveness of the proposed SPAN for person re-identification. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-021-11302-3 | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Person re-identification, Deep learning, Neural network, Verification loss, Feature learning | Journal | 80 |
Issue | ISSN | Citations |
25 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |