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Conference Paper: Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

TitleOrientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification
Authors
Issue Date2017
Citation
Proceedings of the IEEE International Conference on Computer Vision, 2017, v. 2017-October, p. 379-387 How to Cite?
AbstractIn this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
Persistent Identifierhttp://hdl.handle.net/10722/316489
ISSN
2023 SCImago Journal Rankings: 12.263
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Zhongdao-
dc.contributor.authorTang, Luming-
dc.contributor.authorLiu, Xihui-
dc.contributor.authorYao, Zhuliang-
dc.contributor.authorYi, Shuai-
dc.contributor.authorShao, Jing-
dc.contributor.authorYan, Junjie-
dc.contributor.authorWang, Shengjin-
dc.contributor.authorLi, Hongsheng-
dc.contributor.authorWang, Xiaogang-
dc.date.accessioned2022-09-14T11:40:34Z-
dc.date.available2022-09-14T11:40:34Z-
dc.date.issued2017-
dc.identifier.citationProceedings of the IEEE International Conference on Computer Vision, 2017, v. 2017-October, p. 379-387-
dc.identifier.issn1550-5499-
dc.identifier.urihttp://hdl.handle.net/10722/316489-
dc.description.abstractIn this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.-
dc.languageeng-
dc.relation.ispartofProceedings of the IEEE International Conference on Computer Vision-
dc.titleOrientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICCV.2017.49-
dc.identifier.scopuseid_2-s2.0-85041894694-
dc.identifier.volume2017-October-
dc.identifier.spage379-
dc.identifier.epage387-
dc.identifier.isiWOS:000425498400040-

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