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首页> 外文期刊>International Journal on Computer Science and Engineering >Fast Pedestrian Detection using Smart ROI separation and Integral image based Feature Extraction
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Fast Pedestrian Detection using Smart ROI separation and Integral image based Feature Extraction

机译：使用智能ROI分离和基于积分图像的特征提取进行快速行人检测

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This paper discusses a fast pedestrian detection system for near infrared imaging system. The Advanced Driver Assistance Systems include pedestrian detection system to avoid accidents. Most pedestrian detection systems produce false alarms or they are not fast. To overcome these issues a new approach for pedestrian detection is presented here. Initially the foreground is segmented by a smart region detection method to generate candidates. Then a series of rejecters are integrated to filter out non-pedestrians. After filtering out typical non-pedestrian objects, the remaining number of region of interest (ROI) is verified using a Support Vector Machine (SVM) classifier with Histogram of Oriented Gradients (HOG) feature. A second level classification is performed with HAAR feature to reduce the False alarms. The integral image representation is used for extracting both features, which significantly improves the computation speed. Experimental result shows that the proposed pedestrian detection system is suitable in the real-time environment, as it gives high detection rate and very low false alarm rate.
机译：本文讨论了一种用于近红外成像系统的快速行人检测系统。先进的驾驶员辅助系统包括行人检测系统，可避免发生事故。大多数行人检测系统会产生错误警报，或者警报速度不快。为了克服这些问题，这里提出了一种行人检测的新方法。最初，前景通过智能区域检测方法进行分段以生成候选对象。然后集成了一系列拒绝器，以过滤掉非行人。滤除典型的非行人对象后，使用具有定向梯度直方图（HOG）功能的支持向量机（SVM）分类器来验证感兴趣区域（ROI）的剩余数量。使用HAAR功能执行第二级分类，以减少误报。积分图像表示用于提取两个特征，从而显着提高了计算速度。实验结果表明，所提出的行人检测系统具有较高的检测率和非常低的误报率，因此适用于实时环境。

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