Redefining Human Pose Estimation: A Novel Approach with Keypoint Detection
Title: Redefining Human Pose Estimation: A Novel Approach with Keypoint Detection
Description:
Redefining Human Pose Estimation: A Novel Approach with Keypoint Detection
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Objective:
To propose an hour glas based DL approach for accurately predicting and estimating human poses.
Proposed Work:
This research endeavors to enhance human pose estimation by leveraging a dataset comprising multiple body keypoints. Drawing inspiration from the hourglass network architecture, a novel scheme is proposed to address the limitations of current systems and elevate the accuracy and performance of the pose estimation model. The novel architecture encompasses an initial downsampling stage followed by an upsampling stage, facilitating the extraction of intricate details from the input samples. This innovative design enables the system to handle various joints, including the shoulder, elbow, wrist, and more, with heightened precision and reliability. By implementing this advanced architecture, the developed system significantly improves the accuracy and robustness of human pose estimation. By effectively capturing and analyzing the finer nuances of body movements and configurations, the model excels in detecting and delineating human body keypoints with greater fidelity and accuracy. Furthermore, the enhanced architecture enables the model to overcome the limitations inherent in traditional Convolutional Neural Network (CNN) based systems. Thus, this innovative approach not only addresses the challenges faced by current pose estimation models but also paves the way for more precise and reliable applications in action recognition, sports analysis, surveillance, and human-computer interaction.
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