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|Scopus||Web of Science®||Altmetric|
|Title:||Improved human detection and classification in thermal images|
|Citation:||Proceedings of IEEE 17th International Conference on Image Processing (ICIP 2010), 26-29th September, 2010;. pp.2313-2316|
|Publisher Place:||Hong Kong|
|Conference Name:||IEEE International Conference on Image Processing (ICIP) (17th : 2010 : Hong Kong)|
|Weihong Wang, Jian Zhang and Chunhua Shen|
|Abstract:||We 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.|
|Rights:||© Copyright 2010 IEEE – All Rights Reserved|
|Appears in Collections:||Computer Science publications|
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