Combined Top-Down/Bottom-Up Human Articulated Pose Estimation Using AdaBoost Learning

In this paper, a novel human articulated pose estimation method based on AdaBoost algorithm is presented. The human articulated pose is estimated by locating major human joint positions. We learn the classifiers on a normalized image for classifying each pixel position into a certain category. Two different kinds of classifiers, bottom-up joint position classifier and top-down skeleton classifier, are combined to achieve final results. HOG (Histogram of Oriented Gradient) feature is used for training both classifiers. Our human pose estimation system consists of three models, human detection, view classification, and pose estimation. The implemented system can automatically estimate human pose of different views. Experiment results are reported to show our proposed method can work on relatively small-size human images without using human silhouettes as a prerequisite, which is very efficient, robust and accurate enough for potential applications in visual surveillance.