Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas

Yucel Uzun, M. Balcilar, Khudaydad Mahmoodi, Feruz Davletov, M. Amasyali, S. Yavuz
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引用次数: 15

Abstract

Employing robot teams at disaster areas requires usage of autonomous navigation methods. Moreover, autonomous navigation requires autonomous victim detection. Human skin color based victim detection methods may not be robust due to the variations in lightening conditions at disaster areas. Histograms of Oriented Gradients (HoG) were presented as an alternative way of human detection. In literature, HoG based methods proved their efficiency on the datasets including upright humans. But, the victims have very large variation of poses at a disaster area. In this work, the efficiency of HoG based methods was investigated on a dataset including very different poses and lightening conditions. We have reached 95% success on automatic victim detection problem in real time simulation environment.
HoG(定向梯度直方图)特征在灾区受害者检测中的应用
在灾区雇用机器人团队需要使用自主导航方法。此外,自主导航需要自主检测受害者。由于灾区光照条件的变化,基于人类肤色的受害者检测方法可能不太可靠。定向梯度直方图(HoG)被提出作为人类检测的一种替代方法。在文献中,基于HoG的方法在包括直立人类在内的数据集上证明了它们的有效性。但是,受害者在灾区的姿势变化很大。在这项工作中,研究了基于HoG的方法在包含非常不同的姿势和光照条件的数据集上的效率。在实时仿真环境下,自动检测受害者的成功率达到95%。
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