Pedestrian detection based on gradient and texture feature integration

NEUROCOMPUTING

卷:228

Article

Zheng, Chun-Hou^1,3  Pei, Wen-Juan¹  Yan, Qing¹  Chong, Yan-Wen² 

[1] Anhui Univ, Coll Elect Engn & Automat, Hefei, Peoples R China

[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China

[3] Anhui Univ, Ctr Informat Support & Assurance Technol, Hefei, Peoples R China

关键词: Pedestrian detection;    Local binary patterns;    Histogram of oriented gradient;    Sparse representation;    K-SVD;   

DOI  :  10.1016/j.neucom.2016.09.085

来源: Elsevier

【 摘 要 】

In this paper, based on feature integration, we proposed a new method for pedestrian detection. Firstly, we extracted the histogram of oriented gradients (HOG) feature and local binary pattern (LBP) feature from the original images respectively. Secendly, K-singular value decomposition (K-SVD) was used to extract sparse representation features from the HOG and LBP features. Moreover, PCA was used to reduce the dimension of HOG and LBP. Finally, we combined the PCA based features and the K-SVD based sparse representation features directly for fast pedestrian detection in still images. Experimental results on two databases show that the proposed approach is effective for pedestrian detection.

【 授权许可】

Free