LAUSR

Three-dimensional (3D) human skeleton extraction is a powerful tool for activity acquirement and analyses, spawning a variety of applications on somatosensory control, virtual reality and many prospering fields. However, the 3D human skeletonization relies heavily on RGB-Depth (RGB-D) cameras, expensive wearable sensors and specific lightening conditions, resulting in great limitation of its outdoor applications. This paper presents a novel 3D human skeleton extraction method designed for the monocular camera large scale outdoor scenarios. The proposed algorithm aggregates spatial–temporal discrete joint positions extracted from human shadow on the ground. Firstly, the projected silhouette information is recovered from human shadow on the ground for each frame, followed by the extraction of two-dimensional (2D) joint projected positions. Then extracted 2D joint positions are categorized into different sets according to activity silhouette categories. Finally, spatial–temporal integration of same-category 2D joint positions is carried out to generate 3D human skeletons. The proposed method proves accurate and efficient in outdoor human skeletonization application based on several comparisons with the traditional RGB-D method. Finally, the application of the proposed method to RGB-D skeletonization enhancement is discussed.