Benchmarking for person re-identification
Advances in Computer Vision and Pattern Recognition, ISSN: 2191-6594, Vol: 56, Page: 333-349
2014
- 7Citations
- 6Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
The evaluation of computer vision and pattern recognition systems is usually a burdensome and time-consuming activity. In this chapter all the benchmarks publicly available for re-identification will be reviewed and compared, starting from the ancestors VIPeR and Caviar to the most recent datasets for 3D modeling such as SARC3d (with calibrated cameras) and RGBD-ID (with range sensors). Specific requirements and constraints are highlighted and reported for each of the described collections. In addition, details on themetrics that aremostly used to test and evaluate the re-identification systems are provided.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84986204871&origin=inward; http://dx.doi.org/10.1007/978-1-4471-6296-4_16; https://link.springer.com/10.1007/978-1-4471-6296-4_16; https://dx.doi.org/10.1007/978-1-4471-6296-4_16; https://link.springer.com/chapter/10.1007/978-1-4471-6296-4_16
Springer Science and Business Media LLC
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