NDLI: Human head detection based on multi-stage CNN with voting strategy

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Human head detection based on multi-stage CNN with voting strategy

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Human head detection based on multi-stage CNN with voting strategy

Content Provider	ACM Digital Library
Author	Fei, Jianchao Tang, Jian Fang, Husheng Zhou, You Rui, Ting
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.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450335287
DOI	10.1145/2808492.2808560
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Abstract feature of multi-stage Convolutional neural network Human head detection Voting strategy
Content Type	Text
Resource Type	Article

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