Real-Time Gesture Recognition Using Optical Flow and Deep Learning
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Abstract
Gesture recognition has applications in human-computer interaction, gaming, and virtual reality. This study proposes a novel approach that combines optical flow-based motion tracking with deep learning techniques for real-time gesture recognition. By analyzing the motion of keypoints within the frame using optical flow, we train a deep neural network to classify gestures with high accuracy. The proposed system outperforms traditional methods by reducing processing time and increasing robustness to noise, making it suitable for real-time applications.
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References
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