Late Fusion in Part-based Person Re-identification

Aske Rasch Lejbølle, Kamal Nasrollahi, Thomas B. Moeslund

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

7 Citationer (Scopus)
8 Downloads (Pure)

Abstract

In person re-identification, the purpose is to match persons across, typically, non-overlapping cameras. This introduces challenges such as occlusion and changes in view and light- ing. In order to overcome these challenges, discriminative features are extracted and used in combination with a su- pervised metric learning algorithm. Most often, feature rep- resentations are created from the entire body, causing noisy features if certain parts are occluded. Therefore, we propose a system which applies the same learning algorithm sepa- rately on feature representations from different body parts and late fuses the outputs, to take advantage of situations in which features from certain body parts are more discrim- inative than other. By evaluation on features at three ab- straction levels, we show that the proposed system increase accuracy by up to 19.87% in the case of high-level features. In addition, we also fuse the features at different abstraction levels to further improve results. Experimental results on VIPeR and CUHK03 show similar performance to state-of- the-art with rank-1 accuracies of 52.72% and 61.50%, respec- tively, while results on the datasets PRID450S and CUHK01 show rank-1 accuracies of 78.36% and 73.40%, respectively, improvements of 11.74% and 7.76% compared to state-of- the-art.
OriginalsprogEngelsk
TitelProceedings of the 9th International Conference on Machine Learning and Computing
ForlagAssociation for Computing Machinery
Publikationsdatojun. 2017
Sider385-393
ISBN (Trykt)978-1-4503-4817-1
DOI
StatusUdgivet - jun. 2017
BegivenhedInternational Conference on Machine Learning and Computing - Singapore, Singapore
Varighed: 24 feb. 201726 feb. 2017
Konferencens nummer: 9

Konference

KonferenceInternational Conference on Machine Learning and Computing
Nummer9
Land/OmrådeSingapore
BySingapore
Periode24/02/201726/02/2017
NavnACM International Conference Proceedings Series

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