Cited By
View all- Li LMa J(2018)Zenithal People Detection Based on Improved Faster R-CNN2018 IEEE 4th International Conference on Computer and Communications (ICCC)10.1109/CompComm.2018.8780807(1503-1508)Online publication date: Dec-2018
Human head detection based on multi-stage CNN with voting strategy
Article No.: 68, Pages 1 - 4
Abstract
As a classical problem in target detection, human head detection based on head features is an important basis of intelligent vehicle driving and people counting. Due to the irregularity and complexity of human head, artificial designed feature description methods have lower recognition rate and worse robustness. As an important part of deep learning, convolutional neural network (CNN) has applied to image recognition and speech analysis successfully. In view of the instability and hard description of head features, a new head detection method based on multi-stage CNN with voting strategy is proposed in this paper. Firstly, we use features abstracted by multi-stage CNN from different layers to classify respectively. Then, we use the results to get the final classification through voting strategy. Experimental results show that new method has higher recognition rate compared with traditional ones.
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- Human head detection based on multi-stage CNN with voting strategy
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Published In
August 2015
397 pages
ISBN:9781450335287
DOI:10.1145/2808492
- General Chairs:
- Ramesh Jain,
- Shuqiang Jiang,
- Program Chairs:
- John Smith,
- Jitao Sang,
- Guohui Li
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 August 2015
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ICIMCS '15
ICIMCS '15: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2015
Hunan, Zhangjiajie, China
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ICIMCS '15 Paper Acceptance Rate 20 of 128 submissions, 16%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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View all- Li LMa J(2018)Zenithal People Detection Based on Improved Faster R-CNN2018 IEEE 4th International Conference on Computer and Communications (ICCC)10.1109/CompComm.2018.8780807(1503-1508)Online publication date: Dec-2018
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