李谦, 张永军, 卢洪树, 刘欣怡. 基于错误式学习的低空影像人行横道多角度自动识别[J]. 武汉大学学报 ( 信息科学版), 2018, 43(1): 46-52. DOI: 10.13203/j.whugis20150342
引用本文: 李谦, 张永军, 卢洪树, 刘欣怡. 基于错误式学习的低空影像人行横道多角度自动识别[J]. 武汉大学学报 ( 信息科学版), 2018, 43(1): 46-52. DOI: 10.13203/j.whugis20150342
LI Qian, ZHANG Yongjun, LU Hongshu, LIU Xinyi. Detection of Pedestrian Crossings with Hierarchical Learning Classifier from Multi-angle Low Altitude Images[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 46-52. DOI: 10.13203/j.whugis20150342
Citation: LI Qian, ZHANG Yongjun, LU Hongshu, LIU Xinyi. Detection of Pedestrian Crossings with Hierarchical Learning Classifier from Multi-angle Low Altitude Images[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 46-52. DOI: 10.13203/j.whugis20150342

基于错误式学习的低空影像人行横道多角度自动识别

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

  • 摘要: 提出了一种基于特征的错误式学习分类器半自动迭代训练方法,该分类器能够自动识别多角度低空影像上的人行横道线,在人行横道线管控与数据库建立、道路网提取上有较好的应用。介绍了基于错误式学习的分类器训练思路与方法,并提出了将同一地区不同角度低空影像的识别结果进行合并,从而尽可能全面的检测出被城区高楼以及车辆遮挡的人行横道线的思路。通过对比实验该方法的鲁棒性,并在其基础上随机选取多组数据进行系列实验,证实了基于错误式学习的分类器比传统方式训练的分类器有更好的综合性能,能够在不降低识别耗时的前提下产生高准确率、低漏检率和低误识别率的识别结果。

     

    Abstract: 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.

     

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