Human Detection by Combining Histograms of Oriented Gradients with Global Feature

• Main Authors: Hsin-Ming Tsai, 蔡欣明
• Other Authors: Shih-Shinh Huang
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/31908168846492052200

碩士 === 國立高雄第一科技大學 === 電腦與通訊工程所 === 97 === In this work, we propose an algorithm of combining Histograms of Oriented Gradients(HOG) with global feature for human detection from a non-static camera. We use AdaBoost algorithm to learn local characteristics of human based on HOG by giving massive training samples. The human detector is represented by a set of selected discriminative HOG features. Since local feature is easily affected by complex backgrounds and noise, the idea of this work is to incorporate the global feature for improving the detection accuracy. Here, we adopt the head contour as the global feature. The score for evaluating the existence of the head contour is through the Chamfer distance. Furthermore, the score distributions of the pedestrian and non-pedestrian are modeled by Gaussian and Anova distributions, respectively. The combination of the human detector based on local features and head contour is achieved through the adjustment of the hyperplane of support vector machine. In the experiments, we exhibit that our proposed human detection method not only has higher detection rate but also lower false positive rate in comparison with the state-of-the-art human detector.