Computer Science ›› 2014, Vol. 41 ›› Issue (12): 255-259.doi: 10.11896/j.issn.1002-137X.2014.12.055

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Pedestrian Detection with Block Feature Shrink

ZHANG Deng-yi,WANG Qian,GUO Lei and WU Xiao-ping   

  • Online:2018-11-14 Published:2018-11-14

Abstract: To improve the detection rate and decrease the high dimension of histogram of oriented gradient (HOG) and local binary patterns (LBP) features in pedestrian detection,this paper proposed a pedestrian detection method based on block feature shrink.Firstly,the sample image is divided into many overlapped blocks with the same size.Then the HOG and LBP features are abstracted from these blocks,and are fused together as those blocks feature.Next,block classifiers are trained by block features.Those blocks are sorted according to the detection rate of the classifiers.We chose the blocks with higher rate to shrink their features.Finally,the block features are connected after shrinking as the last feature used to detect pedestrian.Experimental results on INRIA test set report that the proposed method has higherdetection rate and lower dimension.

Key words: Pedestrian detection,Feature fusion,Block feature shrink,Histogram of oriented gradient,Local binary patterns

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