Sequence Alignment for RGB-D and Motion Capture Skeletons

Capture de mouvements humains par capteurs RGB-D. (Capture human motions by RGB-D sensor )

Simultaneous apparition of depth and color sensors and super-realtime skeleton detection algorithms led to a surge of new research in Human Motion Capture. This feature is a key part of Human-Machine Interaction. But the applicative context of those new technologies is voluntary, fronto-parallel interaction with the sensor, which allowed the designers certain approximations and requires a speci...

Real-time Action Recognition for RGB--D and Motion Capture Data

Classification of RGB-D and Motion Capture Sequences Using Extreme Learning Machine

In this paper we present a robust motion recognition framework for both motion capture and RGB-D sensor data. We extract four different types of features and apply a temporal difference operation to form the final feature vector for each frame in the motion sequences. The frames are classified with the extreme learning machine, and the final class of an action is obtained by majority voting. We...

Temporally Consistent Motion Segmentation from RGB-D Video

We present a method for temporally consistent motion segmentation from RGB-D videos assuming a piecewise rigid motion model. We formulate global energies over entire RGB-D sequences in terms of the segmentation of each frame into a number of objects, and the rigid motion of each object through the sequence. We develop a novel initialization procedure that clusters feature tracks obtained from t...

RGB-D-based Human Motion Recognition with Deep Learning: A Survey

Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success...