The Strength of Weak Learnability
Machine Learning
Machine Learning
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Autonomous Driving Goes Downtown
IEEE Intelligent Systems
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The treelike assembly classifier for pedestrian detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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In pedestrian detection system, it is critical to determine whether a candidate region contains a pedestrian both quickly and reliably. Therefore, an efficient classifier must be designed. In general, a well-organized assembly classifier outperforms than single classifiers. For pedestrian detection, due to the complexity of scene and vast number of candidate regions, an efficient ensemble method is needed. In this paper, we propose a virus evolutionary genetic algorithm (VEGA) based selective ensemble classifier for pedestrian detection system, in which only part of the trained learners are selected and participate the majority voting for the detection. Component learners are trained with diversity and then VEGA is employed to optimize the selection of component learners. Moreover, a time-spending factor is added to the fitness function so as to balance the detection rate and detection speed. Experiments show that, comparing with typical non-selective Bagging and GA-based selective ensemble method, the VEGA-based selective ensemble gets better performance not only in detecting accuracy but also in detection speed.