6,557 research outputs found
Looking at the Body: Automatic Analysis of Body Gestures and Self-Adaptors in Psychological Distress
Psychological distress is a significant and growing issue in society.
Automatic detection, assessment, and analysis of such distress is an active
area of research. Compared to modalities such as face, head, and vocal,
research investigating the use of the body modality for these tasks is
relatively sparse. This is, in part, due to the limited available datasets and
difficulty in automatically extracting useful body features. Recent advances in
pose estimation and deep learning have enabled new approaches to this modality
and domain. To enable this research, we have collected and analyzed a new
dataset containing full body videos for short interviews and self-reported
distress labels. We propose a novel method to automatically detect
self-adaptors and fidgeting, a subset of self-adaptors that has been shown to
be correlated with psychological distress. We perform analysis on statistical
body gestures and fidgeting features to explore how distress levels affect
participants' behaviors. We then propose a multi-modal approach that combines
different feature representations using Multi-modal Deep Denoising
Auto-Encoders and Improved Fisher Vector Encoding. We demonstrate that our
proposed model, combining audio-visual features with automatically detected
fidgeting behavioral cues, can successfully predict distress levels in a
dataset labeled with self-reported anxiety and depression levels
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
Recently, skeleton based action recognition gains more popularity due to
cost-effective depth sensors coupled with real-time skeleton estimation
algorithms. Traditional approaches based on handcrafted features are limited to
represent the complexity of motion patterns. Recent methods that use Recurrent
Neural Networks (RNN) to handle raw skeletons only focus on the contextual
dependency in the temporal domain and neglect the spatial configurations of
articulated skeletons. In this paper, we propose a novel two-stream RNN
architecture to model both temporal dynamics and spatial configurations for
skeleton based action recognition. We explore two different structures for the
temporal stream: stacked RNN and hierarchical RNN. Hierarchical RNN is designed
according to human body kinematics. We also propose two effective methods to
model the spatial structure by converting the spatial graph into a sequence of
joints. To improve generalization of our model, we further exploit 3D
transformation based data augmentation techniques including rotation and
scaling transformation to transform the 3D coordinates of skeletons during
training. Experiments on 3D action recognition benchmark datasets show that our
method brings a considerable improvement for a variety of actions, i.e.,
generic actions, interaction activities and gestures.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
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