Transforming spatio-temporal self-attention using action embedding for skeleton-based action recognition

Ahmad, Tasweer; Rizvi, Syed Tahir Hussain; Kanwal, Neel

doi:10.1016/j.jvcir.2023.103892

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…Additionally, the document discusses the potential for future research directions, such as evaluating the framework in a real human-robot collaboration task and further investigating the robustness of the framework to different viewpoints considering a multi-camera setup. [4]The article proposes a novel methodology for skeleton-based action recognition, utilizing action embedding and self-attention Transformer. The method consists of two main modules: action embedding and self-attention Transformer.…”

Section: Gesture Recognition For Human-robot Collaborationmentioning

confidence: 99%

Human Action Recognition System Using LRCN

Eldo,

Kuriakose

et al. 2024

IJRASET

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The importance of human action recognition is significant across various domains, driving advancements in safety, healthcare, sports analytics, and interactive technologies. Leveraging machine learning techniques like Long Recurrent Convolutional Neural Networks (LRCN) trained on datasets such as UCF50, our project focuses on action prediction from YouTube videos. This technology plays a pivotal role in enhancingsafety and security by enabling the detection of anomalies and suspicious behaviours in surveillance systems. In healthcare, it supports remote patient monitoring, rehabilitation assessment, and personalized care plans based on observed actions. Addi- tionally, in sports analytics and entertainment, human action recognition informs performance evaluation, content creation, and immersive experiences. Our integration of a pre-trained LRCN model into a Flask web application signifies the tangible impact of machine learning on human action recognition, with ongoing efforts to optimize functionality through input validation, error handling, user feedback, security measures, and performance enhancements, illustrating the practical application and societal benefits of this innovative technology

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Section: Gesture Recognition For Human-robot Collaborationmentioning

confidence: 99%