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 1-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.
Chen
Yu-Ting
YT
Institute of Information Science, Academia Sinica, Taipei, Taiwan. yuhtyng@iis.sinica.edu.tw
Chen
Chu-Song
CS
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D010781:Photography / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D006801:Humans