Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons

Current methods for skeleton-based human action recognition usually work with completely observed skeletons. However, in real scenarios, it is prone to capture incomplete and noisy skeletons, which will deteriorate the performance of traditional models. To enhance the robustness of action recognition models to incomplete skeletons, we propose a multi-stream graph convolutional network (GCN) for exploring sufficient discriminative features distributed over all skeleton joints. Here, each stream of the network is only responsible for learning features from currently unactivated joints, which are distinguished by the class activation maps (CAM) obtained by preceding streams, so that the activated joints of the proposed method are obviously more than traditional methods. Thus, the proposed method is termed richly activated GCN (RA-GCN), where the richly discovered features will improve the robustness of the model. Compared to the state-of-the-art methods, the RA-GCN achieves comparable performance on the NTU RGB+D dataset. Moreover, on a synthetic occlusion dataset, the performance deterioration can be alleviated by the RA-GCN significantly.Comment: Accepted by ICIP 2019, 5 pages, 3 figures, 3 table

Unsupervised Learning of Long-Term Motion Dynamics for Videos

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.Comment: CVPR 201

Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition

Over the past few years, skeleton-based action recognition has attracted great success because the skeleton data is immune to illumination variation, view-point variation, background clutter, scaling, and camera motion. However, effective modeling of the latent information of skeleton data is still a challenging problem. Therefore, in this paper, we propose a novel idea of action embedding with a self-attention Transformer network for skeleton-based action recognition. Our proposed technology mainly comprises of two modules as, i) action embedding and ii) self-attention Transformer. The action embedding encodes the relationship between corresponding body joints (e.g., joints of both hands move together for performing clapping action) and thus captures the spatial features of joints. Meanwhile, temporal features and dependencies of body joints are modeled using Transformer architecture. Our method works in a single-stream (end-to-end) fashion, where MLP is used for classification. We carry out an ablation study and evaluate the performance of our model on a small-scale SYSU-3D dataset and large-scale NTU-RGB+D and NTU-RGB+D 120 datasets where the results establish that our method performs better than other state-of-the-art architectures.publishedVersio

Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition

Capturing the dependencies between joints is critical in skeleton-based action recognition task. Transformer shows great potential to model the correlation of important joints. However, the existing Transformer-based methods cannot capture the correlation of different joints between frames, which the correlation is very useful since different body parts (such as the arms and legs in "long jump") between adjacent frames move together. Focus on this problem, A novel spatio-temporal tuples Transformer (STTFormer) method is proposed. The skeleton sequence is divided into several parts, and several consecutive frames contained in each part are encoded. And then a spatio-temporal tuples self-attention module is proposed to capture the relationship of different joints in consecutive frames. In addition, a feature aggregation module is introduced between non-adjacent frames to enhance the ability to distinguish similar actions. Compared with the state-of-the-art methods, our method achieves better performance on two large-scale datasets.Comment: 14 pages, 5 figure