Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector Machines (SVM) for classification. Recognition is carried out hierarchically by first recognizing people and then individual characters. The characteristic features that best discriminate one person from another are learned automatically. Tracking is solved via a particle filter that utilizes the SVM output and a first order kinematic model to obtain a robust scheme that successfully handles occlusion, different poses and camera zooms. For pose estimation a collection of SVM classifiers is evaluated to detect specific, learned poses.