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Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

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成果类型:
期刊论文
作者:
Zhang, Yang*;Xin, Dong-Rong;Wu, Yi-Hu
通讯作者:
Zhang, Yang
作者机构:
[Xin, Dong-Rong; Zhang, Yang] Fujian Univ Technol, Sch Transportat, Fuzhou, Fujian Province, Peoples R China.
[Wu, Yi-Hu] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha, Hunan, Peoples R China.
通讯机构:
[Zhang, Yang] F
Fujian Univ Technol, Sch Transportat, Fuzhou, Fujian Province, Peoples R China.
语种:
英文
关键词:
Traffic safety;pedestrian protection;deep belief network;ABH;RBM;T distribution;SVM;experiments
期刊:
Transportation Planning and Technology
ISSN:
0308-1060
年:
2016
卷:
39
期:
8
页码:
791-800
基金类别:
Natural Science Foundation of FujianNatural Science Foundation of Fujian Province [2015J05118, 2015J05001, 2016J01725]; Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51278066, 61304210]
机构署名:
本校为其他机构
院系归属:
交通运输工程学院
摘要:
In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on ...

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