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Detection of Moving Objects in Two Dimensional Plane By Hand Gesture Recognition Using Convolution Neural Networks | IEEE Conference Publication | IEEE Xplore

Detection of Moving Objects in Two Dimensional Plane By Hand Gesture Recognition Using Convolution Neural Networks


Abstract:

this paper details a method for precisely controlling the two-dimensional spatial orientation of any electrical instrument using only hand gestures. To obtain a reasonabl...Show More

Abstract:

this paper details a method for precisely controlling the two-dimensional spatial orientation of any electrical instrument using only hand gestures. To obtain a reasonable idea of where the user’s hand is in respect to the touched object, hand gesture detection makes use of methods like Haar Cascade and CNN. Any computer with an inexpensive webcam should be able to use it. Many other metrics are used to examine and review the results, including testing, sensitivity, distance, execution time, angle, positive and negative predictive value, and many more. The overall performance of the CNN training results is exemplary, with an accuracy of up to 95 \%. Validation results, however, are often within 10 \% of the true value. One key method for accurate gesture recognition is the Convolution Neural Network (CNN) methodology.
Date of Conference: 05-07 June 2024
Date Added to IEEE Xplore: 30 September 2024
ISBN Information:
Conference Location: Raigarh, India

References

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