Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Geomatics and Information Science of Wuhan University》 2018-01
Add to Favorite Get Latest Update

Detection of Pedestrian Crossings with Hierarchical Learning Classifier from Multi-angle Low Altitude Images

LI Qian;ZHANG Yongjun;LU Hongshu;LIU Xinyi;School of Remote Sensing and Information Engineering,Wuhan University;Wuhan Zhongyuan Electronics Group Co.,LTD;School of Electronic Science,National University of Defense Technology;  
This paper proposes a new training method for feature-based iterative hierarchical learning classifiers.It can be used to detect pedestrian crossings from multi-angle low altitude images.The training procedure and the method for merging multi-angle detection results are introduced in this paper.The performance of the classifier was evaluated based on random testing results.Experimental results from several datasets show that the iterative classifier has higher correctness,lower missing rate and lower error rate than the general classifier.Furthermore,the proposed method will not reduce the detection speed.
【Fund】: 国家自然科学基金(41322010);; 中央高校基本科研业务费专项资金(2014213020201)~~
【CateGory Index】: U491
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved