This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Real-time human detection in urban scenes: Local descriptors and classifiers selection with AdaBoost-like algorithms
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
J. Begard, CEA, LIST, Embedded Vision Systems, Boîte Courrier 94, Gif sur Yvette, F-91191 France
N. Allezard, CEA, LIST, Embedded Vision Systems, Boîte Courrier 94, Gif sur Yvette, F-91191 France
P. Sayd, CEA, LIST, Embedded Vision Systems, Boîte Courrier 94, Gif sur Yvette, F-91191 France
This paper deals with the study of various implementations of the AdaBoost algorithm in order to address the issue of real-time pedestrian detection in images. We use gradient-based local descriptors and we combine them to form strong classifiers organized in a cascaded detector. We compare the original AdaBoost algorithm with two other boosting algorithms we developed. One optimizes the use of each selected descriptor to minimize the operations done in the image (method 1), leading to an acceleration of the detection process without any loss in detection performances. The second algorithm (method 2) improves the selection of the descriptors by associating to each of them a more pow erful weak-learner — a decision tree built from the components of the whole descriptor — and by evaluating them locally. We compare the results of these three learning algorithms on a reference database of color images and we then introduce our preliminary results on the adaptation of this detector on infrared vision. Our methods give better detection rates and faster processing than the original boosting algorithm and also provide interesting results for further studies.
Citation:
J. Begard, N. Allezard, P. Sayd, "Real-time human detection in urban scenes: Local descriptors and classifiers selection with AdaBoost-like algorithms," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
Usage of this product signifies your acceptance of the Terms of Use.