结合HOG的密集LDB描述符行人检测

A. J. Das, Navajit Saikia
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引用次数: 5

摘要

近年来,行人检测在众多基于视觉的安全和安保应用中起着至关重要的作用。给定图像,行人检测器从图像中计算特征,并根据特征对是否存在行人进行分类。本文提出了一种新的行人检测特征集,该特征集将改进的局部差分二值特征与定向梯度特征的直方图相结合。采用线性支持向量机作为分类器。提出的检测器的性能是在缺失率相对于FPPI和缺失率相对于FPPW方面,并与现有的类似类型的行人检测器进行比较。研究了探测器的计算效率。
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Pedestrian detection using dense LDB descriptor combined with HOG
Pedestrian detection plays a vital role in numerous vision-based safety and security applications in recent days. Given an image, a pedestrian detector computes features from it and works on the features to classify if there is pedestrian. This paper presents a new feature set for pedestrian detection where a modified version of the local difference binary features are combined with the histogram of oriented gradients features. The linear support vector machine is used as the classifier. The performance of the proposed detector is presented in terms of miss-rate versus FPPI and miss-rate versus FPPW, and is compared with available pedestrian detectors of similar type. The computational efficiency of the detector is also studied.
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