Markerless human pose estimation using image features and extremal contour
Qinghua Liang, Zhenjiang Miao · 2010
This paper presents an approach for markerless vision-based motion capture from multiple views. We use truncated cones to describe the human body parts, and match the extremal contours of human body model against the image cues. Be- cause the derivative is not available, we assume that the cost function satisfies a quadratic model inside the trust region and use model-based Derivative Free Optimization (DFO) method to find a pose which best matches the images. The method performance was tested on the HumanEva II dataset in a 4 color camera configuration and the results show that our method recover the human pose parameterize with high dimensions (≥ 36) effectively.