Pose Estimation Algorithms: History and Evolution

Pose estimation, a computer vision technique that identifies key body joints in images and videos, has evolved significantly since its inception in the late 1960s and early 1970s. Initially grounded in traditional computer vision methods focusing on geometric calculations, the field has advanced through model-based approaches, feature-based methods, and now predominantly employs deep learning-based models for enhanced accuracy and robustness. Notable advancements include algorithms like DeepPose and OpenPose, which utilize convolutional neural networks to achieve state-of-the-art performance. The technique has a broad range of applications, from human-computer interaction and sports analysis to healthcare and realistic animations. Current models such as YOLOv7 Pose and MediaPipe Pose continue to push the boundaries, offering flexibility and efficiency in various scenarios. The development of these models is supported by comprehensive datasets like COCO and MPII, which provide standardized evaluation metrics and annotations. As the field progresses, ongoing research aims to address challenges like occlusions and complex poses to further enhance real-time performance.