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ASME Press Select Proceedings
International Conference on Computer and Electrical Engineering 4th (ICCEE 2011)Available to Purchase
ISBN:
9780791859841
No. of Pages:
698
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
99 Pedestrian Detection Based on Leg Feature Available to Purchase
By
Shaoting Lv
,
Shaoting Lv
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Yong Zhao
,
Yong Zhao
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Ruzhong Cheng
,
Ruzhong Cheng
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Wenfeng Xing
,
Wenfeng Xing
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Jiayao Xu
,
Jiayao Xu
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Xinan Wang
Xinan Wang
School of Computer and Information Engineering,
Peking University Shenzhen Graduate School
, China
, 518055
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Page Count:
5
-
Published:2011
Citation
Lv, S, Zhao, Y, Cheng, R, Xing, W, Xu, J, & Wang, X. "Pedestrian Detection Based on Leg Feature." International Conference on Computer and Electrical Engineering 4th (ICCEE 2011). Ed. Zhou, J. ASME Press, 2011.
Download citation file:
Nowadays the research on Road Safety is receiving more and more attention, and the pedestrian detection is the significant part of such research. Our study focus on the feature of pedestrian's legs because they have an obvious difference from the background when people walking across the road. In paper, we present a novel idea that contributes to low-false alarms and high detection rate. First, we detect the pedestrian based on Haar cascade classifier which is trained by the leg samples. Second, we verify the pedestrian through the HOG descriptors. The results from experiments show our method gets a detection rate of about 87% with few false alarms.
Abstract
Key Words
1 Introduction
2. Related Work
3. Overall Approach
4. Experiment Result
5. Summaries
References
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