ABSTRACT

In order to improve the detection rate of pedestrian, a novel technique that is based on hybrid Gaussian background modeling combined with HOG and SVM is proposed. The random video frame test sample is used to verify the performance of the model. The results show that under the condition of ensuring the detection rate and detection rate, the false detection rate of the hybrid Gauss combined with the HOG + SVM model is only 5.2% in comparison with that of HOG + AdaBoost at 7.1%, which confirms that the model can accurately detect pedestrians in complex scenes in real time.

中文翻译：

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基于混合高斯模型和支持向量机的行人检测

摘要

为了提高行人的检测率，提出了一种基于混合高斯背景建模结合HOG和SVM的新技术。随机视频帧测试样本用于验证模型的性能。结果表明，在保证检出率和检出率的情况下，混合高斯结合HOG+SVM模型的误检率仅为5.2%，而HOG+AdaBoost的误检率为7.1%，证实了该模型可以实时准确地检测复杂场景中的行人。