Improved human detection and classification in thermal images

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 2313 - 2316
Issue Date:
2010-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013006872OK.pdf660.95 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: