Skeleton-based action recognition with temporal action graph and temporal adaptive graph convolution structure

Skeleton-based action recognition has attracted the attention of many researchers. In the process of extracting skeleton features, most methods use the first-order features (the joint position of the skeleton) or the second-order features (the length and direction of the skeleton) to represent the skeleton, while ignoring the importance of skeleton semantic features to action. In this paper, a new skeleton appearance semantic feature is proposed to describe the appearance feature of human skeleton. An attention mechanism of skeleton appearance semantic features is further proposed, which integrates channel features to highlight the overall difference. In addition, a method is used to integrate the semantic features of skeleton appearance with the position and velocity features of skeleton joints, as well as the constructed joints type and frame index, so as to improve the representation method of skeleton semantic features. The experiments were carried out in two public skeleton action recognition data sets (NTU-RGB+D 60 and NTU-RGB+D 120), and the recognition accuracy was higher than that of the baseline model SGN.

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“…HCN [1] 86.5 91.1 VA-CNN [7] 88.7 94.3 SGN [4] 89.0 94.5 ST-AGCN [8] 88. Two-Stream GCA-LSTM [2] 61.2 63.3…”

Section: Comparison Results and Analysismentioning

confidence: 99%

Semantics-enhanced algorithm for skeleton-based action recognition

Dan

Gao

2022

International Conference on Mechanisms and Robotics (ICMAR 2022)

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“…Human target detection methods include background subtraction method, interframe difference method, and optical flow method. Tracking methods include matching-based tracking and motion characteristic-based tracking [5]. Gu et al believe that the technologies related to human posture detection and motion analysis, which are currently in the advanced processing stage, are in the hot stage of exploration and research.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensor and Attitude Analysis of Track and Field Training Action Recognition Based on Artificial Intelligence

Liu

Chang

2022

Journal of Sensors

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In order to provide effective information support to athletics training and to increase the effectiveness of athletics training, in-depth analysis of motion recognition sensors and attitudes based on artificial intelligence was conducted. First of all, the basic conditions of type analysis, cognitive technology, and the current situation were studied, and the basic theory related to it was studied, and on this basis, a human position analysis and recognition system based on artificial intelligence movement training sensors was developed. We studied the technology in depth. Experiments have shown that the approach data collected by the system’s inertia node is transmitted wirelessly to the computer-side software to restore the trend and identify each trend and parameter with high accuracy. During the 30-minute test, the static error was within 1° and the dynamic error was within 5°, which is acceptable and adheres well to dynamic conditions. The system can overcome the limitations of traditional wired or optical methods and be widely used in sports training, human-computer interaction monitoring, rehabilitation medicine, games, film, and television production.

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“…Due to the previously finished research, deep learning methodology has achieved outstanding identification performance and prediction tasks on video-based tasks. Plenty of previously investigated studies [1] are widely working on behavior categorization to identify human actions to generate classification scores from both spatial and temporal perspectives. The research works are based on multiple types of video data, including the skeleton, RGB, RGB+D, optical flow, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

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<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

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Skeleton-based action recognition with temporal action graph and temporal adaptive graph convolution structure

Cited by 17 publications

References 35 publications

Semantics-enhanced algorithm for skeleton-based action recognition

Semantics-enhanced algorithm for skeleton-based action recognition

Sensor and Attitude Analysis of Track and Field Training Action Recognition Based on Artificial Intelligence

Deep Neural Networks in Video Human Action Recognition: A review

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