Three-Dimensional Machine Vision for Robots: Human Activity and Scene Understanding

Dear Colleagues,

Robots have been used in many different areas. Human activity and scene understanding is very important in service robots, because robots should understand the person and environment completely. Human activity and scene understanding is useful in many areas, such as security, surveillance, human–computer interactions, patient monitoring analysis systems, sports, and robotics. With the development of deep learning video analysis techniques for robots, scene understanding, natural language processing, multimodal features (including appearance features, spatial features, and semantic features) based on video frames, skeleton data, and semantic labels have all been used to improve the performance of human activity and scene understanding. Vision transformers and graph models have achieved exemplary performance for a broad range of computer vision tasks, e.g., image recognition, object detection, segmentation, and image captioning. All of these tasks are helpful for robots to develop understanding and perception.

This Special Issue seeks original contributions that help advance the theory and algorithmic design of vision transformers and graph models, and focus on presenting state-of-the-art vision transformers and graph models based on human activity understanding techniques that are developed for solving important problems in 3D robot action/activity recognition, understanding, prediction, and so on.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Human–object interaction recognition;
• Graph models;
• Action recognition;
• Graph neural networks;
• Action predictions;
• Two-/Three-dimensional scene understanding;
• Two-/Three-dimensional object recognition.

Prof. Dr. Liang Zhang
Dr. Guangming Zhu
Guest Editors

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