Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improvement in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extraction, and classification. In spite of giving entire information into feature extraction, the system gives only a useful information (foreground image) by twin background model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For feature extraction, histogram of orientation gradient (HOG) L1 normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized.

中文翻译：

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使用视频分析技术的基于 L1 范数的行人检测

鉴于其对行人保护系统设计的潜在影响，从可见光谱图像中检测行人是一个高度相关的研究领域。一般来说，检测分为两个不同的阶段，特征提取和分类。此外，用于检测行人的功能已经可用，例如最佳特征模型。但仍然需要通过减少执行时间和误报来改进检测。所提出的模型具有三个不同的阶段，即背景减除、特征提取和分类。尽管将整个信息提供给特征提取，但系统通过孪生背景模型仅提供有用信息（前景图像）。然后前景图像移动到特征提取并对行人进行分类。对于特征提取，方向梯度直方图 (HOG) L1 归一化已被使用。这将提高检测精度并减少过程的计算时间。此外，误报率已降至最低。