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.
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References
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© 2005 Springer-Verlag Berlin Heidelberg
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Huang, C., Tang, G., Luo, Y. (2005). Pedestrian Detection by Multiple Decision-Based Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_74
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DOI: https://doi.org/10.1007/11427469_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)