As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed.

Computer vision-based gesture recognition methods play a significant role in robot visual gesture interaction. since of low accuracy leading by insuffcient feature representation and fusion, the existing gesture segmentation and recognition methods fail to meet the requirements of practical applications. To address these issues, a lightweight two-stage end-to-end gesture recognition network called Fusing Gate Dual Stages Network (FGDSNet) is proposed. This network adopts a dual-branch network structure in the segmentation stage. Existing dual-branch network models often directly fuse detailed features and semantic features, which leads to detailed information being obscured by blurry semantic information. Additionally, there are redundant issues in the feature maps at different levels during the network inference process. Therefore, we embed Cosine Similarity-KL Divergence Attention Module (CoSKLAM) and Gate Filtering Module (GFM) between the local detail branch and the contextual semantic branch. The role of these two modules is to facilitate the fusion of local and global features during the feature extraction process and filter out redundant information. Finally, the segmentation result and original gesture image are used as inputs for the recognition network to predict gesture categories. The relevant experiments show that the proposed network performs well in both gesture segmentation and gesture recognition, while also having real-time inference speed and a smaller parameter size.

Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals and interaction noise, in the recommendation task. Therefore, this paper proposes a self-supervised graph attention collaborative filtering for recommendation (SGACF). The correlation between adjacent nodes is deeply mined using a multi-head graph attention network to obtain accurate node representations. It is worth noting that self-supervised learning is brought in as an auxiliary task in the recommendation, where the supervision task is the main task. It assists model training for supervised tasks. A multi-view of a node is generated by the graph data-augmentation method. We maximize the consistency between its different views compared to the views of the same node and minimize the consistency between its different views compared to the views of other nodes. In this paper, the effectiveness of the method is illustrated by abundant experiments on three public datasets. The results show its significant improvement in the accuracy of the long-tail item recommendation and the robustness of the model.

Intelligent perception systems in Human-Machine Interfaces (HMI) can significantly enhance user experience. Consumer electronic devices integrated with crowd counting technology, such as security monitoring and smart homes, not only provide real-time environmental awareness but also offer a more convenient user experience. To reduce the computational burden, several weakly supervised crowd counting frameworks have been proposed. However, existing weakly supervised methods ignore the uneven distribution of crowd and the crucial role of depth information in revealing crowd density. In this paper, we propose a novel depth restoration and density perception with cross-dispersed attention network, termed D3CrowdNet, for crowd counting. D3CrowdNet integrates three key modules: i) the depth perception auxiliary module is designed to reconstruct pseudo-3D spatial information of crowds, incorporating spatial distance and scale to enhance the model’s understanding of crowd distribution. ii) We also develop the density-aware graph construction module, which learns interaction relationships between crowd sub-regions. iii) A single branch multi-head cross-dispersed attention mechanism is developed to enable the network to focus on individuals while simultaneously capturing information about the surrounding crowd. Extensive experiments demonstrate that the proposed method achieves superior performance across six benchmark datasets, particularly achieving state-of-the-art results on the ShanghaiTech, UCF-QNRF, and JHU-Crowd++ datasets.