SIA OpenIR  > 智能检测与装备研究室
Alternative TitleFast human detection algorithm based on BING objectness
郭晓鹏; 杜劲松; 白珈郡; 王伟
Source Publication计算机应用研究
Contribution Rank1
Funding Organization国家重点研发计划资助项目(2016YFB0101101)
Keyword行人检测 二值化规范梯度特征 似物性 梯度方向直方图特征 支持向量机


Other Abstract

To solve the poor real-time performance of traditional pedestrian detection method, this paper proposed a new fast human detection method based on objectness detection. First, this algorithm extracted Binarized normed gradients features from the training image sets , trained a cascade SVM classifier model for objectness estimation, and used this model to product some region of interest, which called candidate windows. Secondly, in order to further improve the quality of original candidate windows, it adjusted the sizes of these windows slightly and applied a rectangle cluster method to them. Finally, this algorithm extracted HOG features of these final candidate windows and employed a SVM classifier to check whether there was a pedestrian. Experimental results demonstrate that the proposed algorithm outperforms the traditional detection models in detection speed while maintaining the comparable detection rate to the traditional detection method.

Document Type期刊论文
Corresponding Author郭晓鹏
Recommended Citation
GB/T 7714
郭晓鹏,杜劲松,白珈郡,等. 基于BING似物性检测的行人快速算法[J]. 计算机应用研究,2018,35(11):3458-3461.
APA 郭晓鹏,杜劲松,白珈郡,&王伟.(2018).基于BING似物性检测的行人快速算法.计算机应用研究,35(11),3458-3461.
MLA 郭晓鹏,et al."基于BING似物性检测的行人快速算法".计算机应用研究 35.11(2018):3458-3461.
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