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Cascade Classifier Using Combination of Histograms of Oriented Gradients for Rapid Pedestrian Detection

Accurate and efficient human detection has become an important area for research in computer vision. In order to solve problems in the past human detection algorithms such as features with fixed sizes, fixed positions and fixed number, the human detection based on united Hogs algorithm was proposed. This algorithm can dynamically generate the features closer to human body contours. Basically maintaining the detection speed, the detection accuracy was improved by our algorithm. We demonstrate that comparing with human detection algorithm using haar, traditional hogs and hogs with variable-size blocks, our algorithm is better in both of detection rate and false positive rate.