An Evolutionary Support Vector Machines Classifier for Pedestrian Detection

In a pedestrian detection system, a classifier is usually designed to recognize whether a candidate is a pedestrian. Support vector machines (SVM) has become a primary technique to train a classifier for pedestrian detection. However, it is hard to give the best training model which has a tremendous...

Full description

Saved in:
Bibliographic Details
Published in2006 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 4223 - 4227
Main Authors Chen, D., Cao, X.B., Xu, Y.W., Qiao, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2006
Subjects
Cameras
Classifier
Computer science
Decoding
Design optimization
Genetic algorithm
Genetic algorithms
Intelligent robots
Kernel
Laboratories
Pedestrian detection system
Redial Base Function
Support vector machine
Support vector machine classification
Support vector machines
Online AccessGet full text

Cover

More Information
Summary:In a pedestrian detection system, a classifier is usually designed to recognize whether a candidate is a pedestrian. Support vector machines (SVM) has become a primary technique to train a classifier for pedestrian detection. However, it is hard to give the best training model which has a tremendous effect to the performance of a SVM classifier. In this paper, we design special code/decode scheme and evaluation function for a training model firstly; and then use genetic algorithm to optimize key parameters which represent the SVM training model. Therefore a most suitable SVM classifier can be obtained for pedestrian detection. Experiments have been carried out in a single camera based pedestrian detection system. The results show that the evolutionary SVM classifier has a better detection rate; moreover, RBF kernel is more suitable than polynomial kernel when chosen in an evolutionary SVM classifier for pedestrian detection
ISBN:9781424402588
1424402581
ISSN:2153-0858
2153-0866
DOI:10.1109/IROS.2006.281917