Keyed In Motion: TensorFlow Deep Learning for Human Action Recognition from Single Images and Video Snapshots Using OpenPose Keypoints

Registration ID: IJNRD_218920

Published ID: IJNRD2405015

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RAVINDRA CHAUHAN, NITIN GOYAL

Neural Network Classifier, Action Recognition, Deep Learning, Model Optimization, Inference Engine

This study constructs a system for recognizing human actions based on a single image or video capture snapshot. Utilizing Tensor Flow Deep Learning models, the system is designed using human keypoints generated through OpenPose. Four classifiers are explored: Neural Network, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) Classifiers. The models' input layer comprises 50 points derived from the x and y coordinates of 25 keypoints obtained from OpenPose, while the output layer represents 11 numerical labels for human actions: 'hand-wave', 'jump', 'leg-cross', 'plank', 'ride', 'run', 'sit', 'lay-down', 'squat', 'stand', and 'walk'. A dataset of 2132 images is employed for both model training and testing. The findings reveal the top-performing classifier models: the Neural Network Classifier with 512 hidden nodes achieves an accuracy of 0.7733, while the Random Forest Classifier with 60 estimators achieves an accuracy of 0.7752. Subsequently, these models are employed as inference engines to identify human actions in both images and real-time videos

Published Paper   E-Certificate

RAVINDRA CHAUHAN & NITIN GOYAL (May-2024). Keyed In Motion: TensorFlow Deep Learning for Human Action Recognition from Single Images and Video Snapshots Using OpenPose Keypoints. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), a156-a170. https://ijnrd.org/papers/IJNRD2405015.pdf

Volume 9 Issue 5, May-2024

Pages : a156-a170

Paper Reg. ID: IJNRD_218920

Published Paper Id: IJNRD2405015

Downloads: 000122025

Research Area: Engineering

Author Type: Indian Author

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405015.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405015

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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