Pedestrian detection by multiple decision-based neural networks

Authors:
Chen Huang;Guangrong Tang;Yupin Luo
Affiliations:
Dept. of Automation, Tsinghua University, Beijing, China;Dept. of Automation, Tsinghua University, Beijing, China;Dept. of Automation, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 4
Cited 0

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Decision-based neural networks with signal/image classification applications

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Abstract

This paper describes an approach of pedestrian detection for onboard application in night driving. Based on single-frame analysis, two-stage method is designed for detecting pedestrians in the cluttered scenes, which are obtained via a normal camera installed on moving vehicle. In the first stage, bright foreground objects are extracted from dim background as candidates. In the second one, we cascade different-feature-based classifiers, emphasizing shape-based classification. Novel contributions of this paper are, 1) developing the shape representation of candidates; 2) combining multiple Decision-Based Neural Networks for elaborate classification, and further reducing the false alarms. Experiments show that our approach is promising, while the system can achieve real-time detection.