16 research outputs found
Co-occurrence Feature Learning for Skeleton based Action Recognition using Regularized Deep LSTM Networks
Skeleton based action recognition distinguishes human actions using the
trajectories of skeleton joints, which provide a very good representation for
describing actions. Considering that recurrent neural networks (RNNs) with Long
Short-Term Memory (LSTM) can learn feature representations and model long-term
temporal dependencies automatically, we propose an end-to-end fully connected
deep LSTM network for skeleton based action recognition. Inspired by the
observation that the co-occurrences of the joints intrinsically characterize
human actions, we take the skeleton as the input at each time slot and
introduce a novel regularization scheme to learn the co-occurrence features of
skeleton joints. To train the deep LSTM network effectively, we propose a new
dropout algorithm which simultaneously operates on the gates, cells, and output
responses of the LSTM neurons. Experimental results on three human action
recognition datasets consistently demonstrate the effectiveness of the proposed
model.Comment: AAAI 2016 conferenc
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
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for action recognition, and the irrelevant joints often bring noise
which can degrade the performance, we need to pay more attention to the
informative ones. However, the original LSTM network does not have explicit
attention ability. In this paper, we propose a new class of LSTM network,
Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action
recognition. This network is capable of selectively focusing on the informative
joints in each frame of each skeleton sequence by using a global context memory
cell. To further improve the attention capability of our network, we also
introduce a recurrent attention mechanism, with which the attention performance
of the network can be enhanced progressively. Moreover, we propose a stepwise
training scheme in order to train our network effectively. Our approach
achieves state-of-the-art performance on five challenging benchmark datasets
for skeleton based action recognition
Skeleton-based Relational Reasoning for Group Activity Analysis
Research on group activity recognition mostly leans on the standard
two-stream approach (RGB and Optical Flow) as their input features. Few have
explored explicit pose information, with none using it directly to reason about
the persons interactions. In this paper, we leverage the skeleton information
to learn the interactions between the individuals straight from it. With our
proposed method GIRN, multiple relationship types are inferred from independent
modules, that describe the relations between the body joints pair-by-pair.
Additionally to the joints relations, we also experiment with the previously
unexplored relationship between individuals and relevant objects (e.g.
volleyball). The individuals distinct relations are then merged through an
attention mechanism, that gives more importance to those individuals more
relevant for distinguishing the group activity. We evaluate our method in the
Volleyball dataset, obtaining competitive results to the state-of-the-art. Our
experiments demonstrate the potential of skeleton-based approaches for modeling
multi-person interactions.Comment: 26 pages, 5 figures, accepted manuscript in Elsevier Pattern
Recognition, minor writing revisions and new reference