Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. [The dataset is available at: this http URL]
Some approaches used recurrent neural networks (RNNs) to model the motion dynamics and context dependencies for 3D human action recognition. Du al @cite_104 proposed a multi-layer RNN framework for 3D action recognition based on a hierarchy of skeleton-based inputs. Liu al @cite_42 introduced a Spatio-Temporal LSTM network by modeling the context information in both temporal and spatial dimensions. Zhang al @cite_71 added a view-adaptation scheme to the LSTM network to regulate the observation viewpoints. Luo al @cite_66 proposed an unsupervised learning method by using a LSTM encoder-decoder framework for action recognition in RGB+D videos.
@cite_42 @cite_66 @cite_71 @cite_104
2779380177 2803158089 2606294640 2953181561
Recent studies demonstrate the effectiveness of Recurrent Neural Networks (RNNs) for action recognition in videos. However, previous works mainly utilize video-level category as supervision to train RNNs, which may prohibit RNNs to learn complex motion structures along time. In this paper, we propose a recurrent pose-attention network (RPAN) to address this challenge, where we introduce a novel pose-attention mechanism to adaptively learn pose-related features at every time-step action prediction of RNNs. More specifically, we make three main contributions in this paper. Firstly, unlike previous works on pose-related action recognition, our RPAN is an end-toend recurrent network which can exploit important spatialtemporal evolutions of human pose to assist action recognition in a unified framework. Secondly, instead of learning individual human-joint features separately, our poseattention mechanism learns robust human-part features by sharing attention parameters partially on the semanticallyrelated human joints. These human-part features are then fed into the human-part pooling layer to construct a highlydiscriminative pose-related representation for temporal action modeling. Thirdly, one important byproduct of our RPAN is pose estimation in videos, which can be used for coarse pose annotation in action videos. We evaluate the proposed RPAN quantitatively and qualitatively on two popular benchmarks, i.e., Sub-JHMDB and PennAction. Experimental results show that RPAN outperforms the recent state-of-the-art methods on these challenging datasets. Recently, skeleton-based action recognition becomes popular owing to the development of cost-effective depth sensors and fast pose estimation algorithms. Traditional methods based on pose descriptors often fail on large-scale datasets due to the limited representation of engineered features. Recent recurrent neural networks (RNN) based approaches mostly focus on the temporal evolution of body joints and neglect the geometric relations. In this paper, we aim to leverage the geometric relations among joints for action recognition. We introduce three primitive geometries: joints, edges, and surfaces. Accordingly, a generic end-to-end RNN based network is designed to accommodate the three inputs. For action recognition, a novel viewpoint transformation layer and temporal dropout layers are utilized in the RNN based network to learn robust representations. And for action detection, we first perform frame-wise action classification, then exploit a novel multi-scale sliding window algorithm. Experiments on the large-scale 3D action recognition benchmark datasets show that joints, edges, and surfaces are effective and complementary for different actions. Our approaches dramatically outperform the existing state-of-the-art methods for both tasks of action recognition and action detection. 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. 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.