This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment th...This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment the human silhouette into different body part sub-regions and extract the primary joints. Second, we convert the distribution of the joints to the feature vector and apply the regression forest to estimate human pose parameters. Third, we apply the temporal constraints mechanism to find the best human pose parameter with the minimum estimation error. In experiments, we show that our system can operate in real-time with sufficient accuracy.展开更多
基金supported by“MOST”under Grant No.103-2221-E-468-006-MY2
文摘This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment the human silhouette into different body part sub-regions and extract the primary joints. Second, we convert the distribution of the joints to the feature vector and apply the regression forest to estimate human pose parameters. Third, we apply the temporal constraints mechanism to find the best human pose parameter with the minimum estimation error. In experiments, we show that our system can operate in real-time with sufficient accuracy.
