Fast human detection using a novel boosted cascading structure with meta stages
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based I-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each stage of the cascaded structure. Instead of using the standard boosted cascade, the proposed method employs a novel cascaded structure that exploits both the stage-wise classification information and the interstage cross-reference information. We introduce meta-stages to enhance the detection performance of a boosted cascade. Experiment results show that the proposed approach achieves high detection accuracy and efficiency.
human detection rectangle features real adaboost adaboost meta-stage cascaded feed-forward classifiers edge-density features edge orientation histograms pedestrian detection