An Improvements of Deep Learner Based Human Activity Recognition with the Aid of Graph Convolution Features

Many researchers are now focusing on Human Action Recognition (HAR), which is based on various deep-learning features related to body joints and their trajectories from videos. Among many schemes, Joints and Trajectory-pooled 3D-Deep Geometric Positional Attention-based Hierarchical Bidirectional Recurrent convolutional Descriptors (JTDGPAHBRD) can provide a video descriptor by learning geometric features and trajectories of the body joints. But the spatial-temporal dynamics of the different geometric features of the skeleton structure were not explored deeper. To solve this problem, this article develops the Graph Convolutional Network (GCN) in addition to the JTDGPAHBRD to create a video descriptor for HAR. The GCN can obtain complementary information, such as higher-level spatial-temporal features, between consecutive frames for enhancing end-to-end learning. In addition, to improve feature representation ability, a search space with several adaptive graph components is created. Then, a sampling and computation-effective evolution scheme are applied to explore this space. Moreover, the resultant GCN provides the temporal dynamics of the skeleton pattern, which are fused with the geometric features of the skeleton body joints and trajectory coordinates from the JTDGPAHBRD to create a more effective video descriptor for HAR. Finally, extensive experiments show that the JTDGPAHBRD-GCN model outperforms the existing HAR models on the Penn Action Dataset (PAD)

Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition

Recently, skeleton-based human action has become a hot research topic because the compact representation of human skeletons brings new blood to this research domain. As a result, researchers began to notice the importance of using RGB or other sensors to analyze human action by extracting skeleton information. Leveraging the rapid development of deep learning (DL), a significant number of skeleton-based human action approaches have been presented with fine-designed DL structures recently. However, a well-trained DL model always demands high-quality and sufficient data, which is hard to obtain without costing high expenses and human labor. In this paper, we introduce a novel data augmentation method for skeleton-based action recognition tasks, which can effectively generate high-quality and diverse sequential actions. In order to obtain natural and realistic action sequences, we propose denoising diffusion probabilistic models (DDPMs) that can generate a series of synthetic action sequences, and their generation process is precisely guided by a spatial-temporal transformer (ST-Trans). Experimental results show that our method outperforms the state-of-the-art (SOTA) motion generation approaches on different naturality and diversity metrics. It proves that its high-quality synthetic data can also be effectively deployed to existing action recognition models with significant performance improvement

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

EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning

Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle. However, such equivariance and invariance properties are overlooked by most existing methods. To fill this gap, we propose EqMotion, an efficient equivariant motion prediction model with invariant interaction reasoning. To achieve motion equivariance, we propose an equivariant geometric feature learning module to learn a Euclidean transformable feature through dedicated designs of equivariant operations. To reason agent's interactions, we propose an invariant interaction reasoning module to achieve a more stable interaction modeling. To further promote more comprehensive motion features, we propose an invariant pattern feature learning module to learn an invariant pattern feature, which cooperates with the equivariant geometric feature to enhance network expressiveness. We conduct experiments for the proposed model on four distinct scenarios: particle dynamics, molecule dynamics, human skeleton motion prediction and pedestrian trajectory prediction. Experimental results show that our method is not only generally applicable, but also achieves state-of-the-art prediction performances on all the four tasks, improving by 24.0/30.1/8.6/9.2%. Code is available at https://github.com/MediaBrain-SJTU/EqMotion.Comment: Accepted to CVPR 202