A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection

We develop a method that can detect humans in a single image based on a new cascaded 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. Instead of the standard boosted cascade, a novel cascaded structure that exploits both the stage-wise classification information and the interstage cross-reference information is proposed. Experimental results show that our approach can detect people with both efficiency and accuracy.