A cascaded classifier for pedestrian detection
Xu, Y. W.; Cao, X. B.; Qiao, H.; Wang, F. Y.
Conference NameIEEE Intelligent Vehicles Symposium
Conference DateJUN 13-15, 2006
Conference PlaceMeguroku, JAPAN
In a pedestrian defection system, the most critical requirement is to quickly and reliably determine whether a candidate region contains a pedestrian. It is essential to design an effective classifier for pedestrian defection. Until now, most of the existing pedestrian detection systems only adopt a single and non-cascaded classifier However, since the scene is complex and the candidate regions are too many (in our experiments, there are more than 40,000 candidate regions); it is difficult to make the recognition both accurate and fast with such a non-cascaded classifier. 
In this paper, we present a cascaded classifier for pedestrian detection. The cascaded classifier combines a statistical learning classifier and a support vector machine classifier. The statistical learning classifier is used to select preliminary candidates, and then the Support vector machine classifier is applied to do a further acknowledgement. This kind of cascaded architecture can take both advantages of the two classifiers, so the detecting rate and defecting speed can be balanced Experimental results illustrate that the cascaded classifier is effective for a real-time detection.
KeywordImage Classification / Learning (Artificial Intelligence / Object Detection / Support Vector Machines / Traffic Engineering Computing / Cascaded Classifier / Pedestrian Detection / Statistical Learning Classifier / Support Vector Machine Classifier / Cameras
Document Type会议论文
Corresponding AuthorXu, Y. W.
AffiliationUniv Sci & Technol China, Dept Comp Sci & Technol
Recommended Citation
GB/T 7714
Xu, Y. W.,Cao, X. B.,Qiao, H.,et al. A cascaded classifier for pedestrian detection[C],2006.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xu, Y. W.]'s Articles
[Cao, X. B.]'s Articles
[Qiao, H.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xu, Y. W.]'s Articles
[Cao, X. B.]'s Articles
[Qiao, H.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xu, Y. W.]'s Articles
[Cao, X. B.]'s Articles
[Qiao, H.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.