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dc.contributor.authorWang, W.en
dc.contributor.authorZhang, J.en
dc.contributor.authorShen, C.en
dc.identifier.citationProceedings of IEEE 17th International Conference on Image Processing (ICIP 2010), 26-29th September, 2010;. pp.2313-2316en
dc.description.abstractWe present a new method for detecting pedestrians in thermal images. The method is based on the Shape Context Descriptor (SCD) with the Adaboost cascade classifier framework. Compared with standard optical images, thermal imaging cameras offer a clear advantage for night-time video surveillance. It is robust on the light changes in day-time. Experiments show that shape context features with boosting classification provide a significant improvement on human detection in thermal images. In this work, we have also compared our proposed method with rectangle features on the public dataset of thermal imagery. Results show that shape context features are much better than the conventional rectangular features on this task.en
dc.description.statementofresponsibilityWeihong Wang, Jian Zhang and Chunhua Shenen
dc.rights© Copyright 2010 IEEE – All Rights Reserveden
dc.titleImproved human detection and classification in thermal imagesen
dc.typeConference paperen
dc.contributor.conferenceIEEE International Conference on Image Processing (ICIP) (17th : 2010 : Hong Kong)en
dc.publisher.placeHong Kongen
pubs.library.collectionComputer Science publicationsen
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
Appears in Collections:Computer Science publications

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