Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Tang, Chao; Hu, Huosheng; Wang, Wenjian; Li, Wei; Peng, Hua

doi:10.1155/2020/4358728

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Similarly, in Wu et al (2022a), the authors proposed a method for the defect identification of foundation piles under layered soil conditions. In Tang et al (2020), a human action recognition scheme was proposed, introducing and using the RGB-D image feature approach, which is a current research hotspot for effectively resisting the influence of external factors and improving the generalization ability of the classifier. The proposed scheme achieved excellent identification results on the public CAD60 and G3D datasets, utilizing three different patterns for human action feature extraction: The RGB modal information, based the histogram of oriented gradient (RGB-HOG), the depth modal information, based on the space-time interest points (D-STIP), and the skeleton modal information based on the joints' relative position feature (S-JRPF).…”

Section: Identification Methodsmentioning

confidence: 99%

Advances on intelligent algorithms for scientific computing: an overview

et al. 2023

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The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

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Section: Identification Methodsmentioning

confidence: 99%

Advances on intelligent algorithms for scientific computing: an overview

et al. 2023

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“…A human action recognition method based on RGB-D image features is proposed. The multimodal information provided by RGB-D sensors is fully utilized to extract valid human action features [27]. A multimodal and multi-level feature extraction method is proposed.…”

Section: Related Workmentioning

confidence: 99%

Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

Sun

Weng

Luo

et al. 2022

IET Image Processing

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With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human-computer interaction more natural and flexible, bringing richer interactive experience to teaching, on-board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB-D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi-level feature fusion of twostream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1-miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

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“…It takes action videos as input, and then builds and trains a machine learning model, and finally classifies the results. Human behavior recognition is also such a process [39]. Therefore, the proposed method can be used in the classification problem that takes action videos as input, especially in human behavior recognition.…”

Section: Practical Applicationmentioning

confidence: 99%

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

Peng

Wang

et al. 2021

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Imitation of human behaviors is one of the effective ways to develop artificial intelligence. Human dancers, standing in front of a mirror, always achieve autonomous aesthetics evaluation on their own dance motions, which are observed from the mirror. Meanwhile, in the visual aesthetics cognition of human brains, space and shape are two important visual elements perceived from motions. Inspired by the above facts, this paper proposes a novel mechanism of automatic aesthetics evaluation of robotic dance motions based on multiple visual feature integration. In the mechanism, a video of robotic dance motion is firstly converted into several kinds of motion history images, and then a spatial feature (ripple space coding) and shape features (Zernike moment and curvature-based Fourier descriptors) are extracted from the optimized motion history images. Based on feature integration, a homogeneous ensemble classifier, which uses three different random forests, is deployed to build a machine aesthetics model, aiming to make the machine possess human aesthetic ability. The feasibility of the proposed mechanism has been verified by simulation experiments, and the experimental results show that our ensemble classifier can achieve a high correct ratio of aesthetics evaluation of 75%. The performance of our mechanism is superior to those of the existing approaches.

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Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Cited by 6 publications

References 36 publications

Advances on intelligent algorithms for scientific computing: an overview

Advances on intelligent algorithms for scientific computing: an overview

Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

Multiple Visual Feature Integration Based Automatic Aesthetics Evaluation of Robotic Dance Motions

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