Marker-less motion capture for biomechanical analysis using the Kinect sensor

Motion capture systems are gaining more and more importance in different fields of research. In the field of biomechanics, marker-based systems have always been used as an accurate and precise method to capture motion. However, attaching markers on the subject is a time-consuming and laborious method. As a consequence, this problem has given rise to a new concept of motion capture based on marker-less systems. By means of these systems, motion can be recorded without attaching any markers to the skin of the subject and capturing colour-depth data of the subject in movement. The current thesis has researched on marker-less motion capture using the Kinect sensor, and has compared the two motion capture systems, marker-based and marker-less, by analysing the results of several captured motions. In this thesis, two takes have been recorded and only motion of the pelvis and lower limb segments have been analysed. The methodology has consisted of capturing the motions using the marker-based and marker-less systems simultaneously and then processing the data by using specific software. At the end, the angles of hip flexion, hip adduction, knee and ankle obtained through the two systems have been compared. In order to obtain the three-dimensional joint angles using the marker-less system, a new software named iPi Soft has been introduced to process the data from the Kinect sensor. Finally, the results of two systems have been compared and thoroughly discussed, so as to assess the accuracy of the Kinect system

RGB-D Scene Flow via Grouping Rigid Motions

Robotics and artificial intelligence have seen drastic advancements in technology and algorithms over the last decade. Computer vision algorithms play a crucial role in enabling robots and machines to understand their environment. A fundamental cue in understanding environments is analyzing the motions within the scene, otherwise known as scene flow. Scene flow estimates the 3D velocity of each imaged point captured by a camera. The 3D information of the scene can be acquired by RGB-D cameras, which produce both colour and depth images and have been proven to be useful for solving many computer vision tasks. Scene flow has numerous applications such as motion segmentation, 3D mapping, robotic navigation and obstacle avoidance, gesture recognition, etc. Most state-of-the-art RGB-D scene flow methods are set in a variational framework and formulated as an energy minimization problem. While these methods are able to provide high accuracy, they are computationally expensive and not robust under larger motions in the scene. The main contributions of this research is a method for efficiently estimating approximate RGB-D scene flow. A new approach to scene flow estimation has been introduced based on matching 3D points from one frame to the next in a hierarchical fashion. One main observation that is used is that most scene motions in everyday life consist of rigid motions. As such, large parts of the scene will follow the same motion. The new method takes advantage of this fact by attempting to group the 3D data in each frame according to like-motions using concepts from spectral clustering. A simple coarse-to-fine voxelization scheme is used to provide fast estimates of motion and accommodate for larger motions. This is a much more tractable approach than existing methods and does not depend on convergence of some defined objective function in an optimization framework. By assuming the scene is composed of rigidly moving parts, non-rigid motions are not accurately estimated and hence the method is an approximate scene flow estimation. Still, quickly determining approximate motions in a scene is tremendously useful for any computer vision tasks that benefit from motion cues. Evaluation is performed on a custom RGB-D dataset because existing RGB-D scene flow datasets presented to date are mostly based on qualitative evaluation. The dataset consists of real scenes that demonstrates realistic scene flow. Experimental results show that the presented method can provide reliable scene flow estimates at significantly faster runtime speed and can handle larger motions better than current methods