Pedestrian detection using heuristic statistics and machine learning

Chia-Chen Li, Pei-Chen Wu, C. Lin
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引用次数: 1

Abstract

Pedestrian detection is an important research field in advanced driver assistance system (ADAS). This paper puts forward a pedestrian detection framework based on both heuristic statistics and machine learning. First, a restriction of region of interest (ROI) is set on the captured image. Second, the template matching coarsely detects candidate pedestrians by using a set of template images, the edge image of the current frame, and the difference image from previous and current frames. Next, the histogram analysis again roughly filters out the candidate pedestrians. Finally, Histogram of Oriented Gradients (HOG) combined with library support vector machine (LIBSVM) is used to verify those candidate pedestrians. The experimental results show that the proposed method can run in real-time, where the false negative rate is 1.43%, and the false positive rate is 0.16%.
使用启发式统计和机器学习的行人检测
行人检测是高级驾驶辅助系统(ADAS)中的一个重要研究领域。提出了一种基于启发式统计和机器学习的行人检测框架。首先,对捕获的图像设置感兴趣区域(ROI)限制。其次,模板匹配利用一组模板图像、当前帧的边缘图像以及与前一帧和当前帧的差值图像对候选行人进行粗检测;接下来,直方图分析再次粗略地过滤出候选行人。最后,结合面向梯度直方图(HOG)和库支持向量机(LIBSVM)对候选行人进行验证。实验结果表明,该方法可以实时运行,假阴性率为1.43%,假阳性率为0.16%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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