Improved human detection and classification in thermal images

Weihong Wang*, Jian Zhang, Chunhua Shen

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    64 Citations (Scopus)

    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 [1]. Results show that shape context features are much better than the conventional rectangular features on this task.

    Original languageEnglish
    Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
    Pages2313-2316
    Number of pages4
    DOIs
    Publication statusPublished - 2010
    Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
    Duration: 26 Sept 201029 Sept 2010

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    ISSN (Print)1522-4880

    Conference

    Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
    Country/TerritoryHong Kong
    CityHong Kong
    Period26/09/1029/09/10

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