Finding Skeletal Structures of Living Entities and Articulated Objects using Unsupervised Learning on Videos

Extracting useful features from images is essential in computer vision. Many algorithms rely on extracting important elements from an image in order to classify what the image contains. Across the years, feature extraction has become more intricate, allowing higher degrees of accuracy in identifying images and seamless constructions of panoramas. One feature that is yet to be fully utilised is the way an object moves in a video, as most existing research into this area has only looked at doing it with a human as the subject, but it has a potential to work for any object that has points of articulation.
Most of the existing research into finding skeletal structures focus only on finding human skeletons and can make the assumption of the shape of a human skeleton beforehand, thus are able to use supervised learning to match the shape to the video that has been provided. The focus of this research is to use a combination of computer vision techniques in order to find the approximate skeletal structure of any object with articulation, with the focus on all types of animals. The reason for using unsupervised learning is that it will allow this to be applied to any species of animal, even though the structures vary vastly so no assumptions of what shapes of body parts from the animal can be made.
Applications of such an approach are wide and varied, and cover numerous interdisciplinary domains including: biological image analysis, analysis of various forms of remote sensing data, medical image analysis (for example detecting body parts, and segmenting connected structures within the body (e.g. the heart and lungs)), autonomous vehicle applications (particularly for detecting and modelling articulated vehicles in video footage, which has major applications in e.g. self-driving cars).