Keysposebased

Keysposebased (also written as key-spose-based) refers to a research paradigm in computer vision and human-computer interaction that models actions, gestures, and interactions primarily through skeletal pose information derived from body keypoints rather than raw image data.

The approach relies on pose estimation systems to detect keypoints (e.g., joints) and represents sequences of poses using coordinates, angles, and limb relationships. A key aspect is preserving temporal dynamics to capture motion patterns.

Common methods include graph-based neural networks that operate on skeletal graphs, convolutional or transformer models that encode sequences of poses, and hybrid systems that fuse pose with limited appearance features.

Applications include action recognition, gesture control, sign language interpretation, sports analytics, and human-robot interaction. It is valued for privacy preservation and robustness to lighting and clothing variations, though accurate pose estimation remains a prerequisite.

Limitations include sensitivity to occlusion and these systems sometimes lack fine-grained information like object interaction or facial expressions unless augmented with additional cues.

Related topics include pose estimation, skeleton-based action recognition, OpenPose, and graph neural networks for skeletal data.