Cascading Rectangle and Edge Orientation Features for Fast Pedestrian Detection

In this paper we develop a pedestrian detection method that can detect human in a single image based on a boosted cascade structure. In our approach, both the rectangle features and 1-D edge-orientation features are employed in the feature pool for weak-learner selection, which can be computed via the integral-image and the integral-histogram techniques, respectively. To make the weak learner more discriminative, Real AdaBoost is used for feature selection and learning the stage classifiers from the training images. Experimental results show that our approach can detect people with both efficiency and accuracy.