2,433 research outputs found
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
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks
This paper addresses the problem of continuous gesture recognition from
sequences of depth maps using convolutional neutral networks (ConvNets). The
proposed method first segments individual gestures from a depth sequence based
on quantity of movement (QOM). For each segmented gesture, an Improved Depth
Motion Map (IDMM), which converts the depth sequence into one image, is
constructed and fed to a ConvNet for recognition. The IDMM effectively encodes
both spatial and temporal information and allows the fine-tuning with existing
ConvNet models for classification without introducing millions of parameters to
learn. The proposed method is evaluated on the Large-scale Continuous Gesture
Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved
the performance of 0.2655 (Mean Jaccard Index) and ranked place in
this challenge
Video-based Sign Language Recognition without Temporal Segmentation
Millions of hearing impaired people around the world routinely use some
variants of sign languages to communicate, thus the automatic translation of a
sign language is meaningful and important. Currently, there are two
sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that
recognizes word by word and continuous SLR that translates entire sentences.
Existing continuous SLR methods typically utilize isolated SLRs as building
blocks, with an extra layer of preprocessing (temporal segmentation) and
another layer of post-processing (sentence synthesis). Unfortunately, temporal
segmentation itself is non-trivial and inevitably propagates errors into
subsequent steps. Worse still, isolated SLR methods typically require strenuous
labeling of each word separately in a sentence, severely limiting the amount of
attainable training data. To address these challenges, we propose a novel
continuous sign recognition framework, the Hierarchical Attention Network with
Latent Space (LS-HAN), which eliminates the preprocessing of temporal
segmentation. The proposed LS-HAN consists of three components: a two-stream
Convolutional Neural Network (CNN) for video feature representation generation,
a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention
Network (HAN) for latent space based recognition. Experiments are carried out
on two large scale datasets. Experimental results demonstrate the effectiveness
of the proposed framework.Comment: 32nd AAAI Conference on Artificial Intelligence (AAAI-18), Feb. 2-7,
2018, New Orleans, Louisiana, US
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