Real-time pedestrian detection via hierarchical convolutional feature
Yang, Dongming1; Zhang, Jiguang2; Xu, Shibiao3; Ge, Shuiying1; Kumar, G. Hemantha2; Zhang, Xiaopeng3
2018-10-01
发表期刊MULTIMEDIA TOOLS AND APPLICATIONS
卷号77期号:19页码:25841-25860
文章类型Article
摘要With the development of pedestrian detection technologies, existing methods can not simultaneously satisfy high quality detection and fast calculation for practical applications. Therefore, the goal of our research is to balance of pedestrian detection in aspects of the accuracy and efficiency, then get a relatively better method compared with current advanced pedestrian detection algorithms. Inspired from recent outstanding multi-category objects detector SSD (Single Shot MultiBox Detector), we proposed a hierarchical convolution based pedestrians detection algorithm, which can provide competitive accuracy of pedestrian detection at real-time speed. In this work, we proposed a fully convolutional network where the features from lower layers are responsible for small-scale pedestrians and the higher layers are for large-scale, which will further improve the recall rate of pedestrians with different scales, especially for small-scale. Meanwhile, a novel prediction box with a single specific aspect ratio is designed to reduce the miss rate and accelerate the speed of pedestrian detection. Then, the original loss function of SSD is also optimized by eliminating interference of the classifier to more adapt pedestrian detection while also reduce the time complexity. Experimental results on Caltech Benchmark demonstrates that our proposed deep model can reach 11.88% average miss rate with the real-time level speed of 20 fps in pedestrian detection compared with current state-of-the-art methods, which can be the most suitable model for practical pedestrian detection applications.
关键词Pedestrian Detection Deep Learning Real-time
WOS标题词Science & Technology ; Technology
DOI10.1007/s11042-018-5819-6
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61620106003 ; (6140001010207) ; 91646207 ; 61671451 ; 61771026 ; 61502490)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000443244400052
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21822
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位1.Chinese Acad Sci, Natl Sci Lib, Beijing, Peoples R China
2.Univ Mysore, Dept Comp Sci, Mysore, Karnataka, India
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
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Yang, Dongming,Zhang, Jiguang,Xu, Shibiao,et al. Real-time pedestrian detection via hierarchical convolutional feature[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2018,77(19):25841-25860.
APA Yang, Dongming,Zhang, Jiguang,Xu, Shibiao,Ge, Shuiying,Kumar, G. Hemantha,&Zhang, Xiaopeng.(2018).Real-time pedestrian detection via hierarchical convolutional feature.MULTIMEDIA TOOLS AND APPLICATIONS,77(19),25841-25860.
MLA Yang, Dongming,et al."Real-time pedestrian detection via hierarchical convolutional feature".MULTIMEDIA TOOLS AND APPLICATIONS 77.19(2018):25841-25860.
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