Segmentation Of Motion Capture Data Research Articles

DeepSegment: Segmentation of motion capture data using deep convolutional neural network

In this paper, we propose a novel framework to segment 3D human motion capture data into distinct behaviors. First, in preprocessing, we build a normalized pose space by eliminating translation and orientation from the 3D poses. We then transform these normalized 3D poses into 2D RGB images, and as a result, we simplify the task of motion segmentation as image classification and recognition. Furthermore, we identify the most significant joints of the skeleton that contribute substantially to executing a motion and get benefits from them by assigning them more weights. The weight allocation to the specific joint has been done purely based on its deviation capability. Finally, each motion is encoded into compact visual representation by exploiting RGB images with weighted joints. We adopt a transfer learning approach to extract a fixed-size feature vector using off-the-shelf deep Convolutional Neural Network (CNN), Alexnet, after fine-tuning. We develop a Kd-tree on these highly descriptive feature vectors to retrieve the nearest neighbors. Based on a similarity measure, we classify the motion segments and ultimately place the cuts on the ongoing motion sequences. We perform extensive experiments to evaluate our proposed approach on popular Motion Capture (MoCap) datasets, CMU and HDM05. Our approach almost outperforms all other state-of-the-art methods, and the results highlight the capabilities of our proposed scheme for effective segmentation.

Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering.

Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.

Low Level Segmentation of Motion Capture Data based on Hierarchical Clustering with Cosine Distance

D motion capture is to track and record human movements. In recent years, it has been applied into many fields, such as human computer interaction, animation, etc. Low- level segmentation of motion capture data is of significance to the various applications of 3D motion capture; however, due to the high dimensionality of motion capture data, traditional low-level segmentation methods can hardly work out a suitable segmentation for motion capture data. In order to solve this problem, a low-level temporal segmentation algorithm based on cosine distance is proposed, hierarchical clustering is explored so that similar velocity vectors are clustered together according to the cosine distance in a progressive way, the center of each cluster is updated as the vector derived with linear regression, the segment boundaries are determined as the point when the cosine distance between adjacent velocity vectors is greater than 1 (angle>90 degrees). We have conducted experiments on the motion capture database provided by Carnegie Mellon University (CMU), the experiment results show that the performance of the proposed method is optimistic.