Published Paper Details:

Multi-person pose estimation under real-world conditions is a complex task. Despite the commendable performance of advanced human detectors, minor inaccuracies in localization and recognition are bound to occur. Such inaccuracies can lead to setbacks in multi-person pose estimation, particularly for approaches heavily reliant on human detection outcomes. This paper explores multi-person pose estimation using the MoveNet model in computer vision, efficiently predicting human body keypoints in images and videos. It comprehensively elaborates the model's architecture, data preprocessing, and keypoint interpretation, demonstrated through code snippets. Empirical evaluation involves diverse datasets and metrics for accuracy and efficiency. Results emphasize the model's effectiveness, especially in complex scenes. The paper also offers insights into multi-person pose estimation, guiding model selection, preprocessing, and evaluation. It aims to promote MoveNet-based solutions across applications, bridging research and practical implementation for impactful real-world deployment.