Pose Estimation for Human Activity Recognition Using Deep Learning on Video Data

Abstract

Pose estimation is a critical task in computer vision, aiming to determine the spatial positions and orientations of objects or individuals within an image or video. This paper introduces a novel approach to pose estimation that leverages deep learning techniques to achieve high accuracy and robustness in diverse environments. We propose a multi-stage convolutional neural network (CNN) that refines pose predictions through iterative processing, significantly enhancing the precision of keypoint localization. The network architecture is complemented by a loss function designed to handle occlusions and ambiguous poses, ensuring reliable performance even in complex scenes.