Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction
المؤلفون المشاركون
Zhong, Qiubo
Zheng, Caiming
Zhang, Haoxiang
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-25
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition.
Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results.
However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions.
In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information.
Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated.
Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity.
Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination.
Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods.
The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhong, Qiubo& Zheng, Caiming& Zhang, Haoxiang. 2020. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhong, Qiubo…[et al.]. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhong, Qiubo& Zheng, Caiming& Zhang, Haoxiang. Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144523
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1144523
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر