Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Virus-evolutionary genetic algorithm based selective ensemble classifier for pedestrian detection
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
The treelike assembly classifier for pedestrian detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
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For a shape-based pedestrian detection system [1], the critical requirement for pedestrian detection from a moving vehicle is to both quickly and reliably determine if a moving figure is a pedestrian. This can be achieved by comparing the candidate pedestrian figure with the given pedestrian templates. However, due to the vast number of templates stored, it is difficult to make the matching process fast and reliable. Therefore many pedestrian detection systems [2, 3, 4] re developed to help the matching process. In this paper, we apply a decomposed SVM algorithm in the matching process which can fulfill the recognition task efficiently.