PoseBased

PoseBased refers to a family of techniques in computer vision and human-computer interaction that rely on estimating and analyzing human body pose to understand actions, gestures, or postures. The core representation is a skeletal model, typically depicting joints and limbs, estimated from images or video, sometimes in 3D.

Technical foundations: Pose is inferred from visual data by detectors that return 2D keypoints or 3D joint locations. Temporal reasoning integrates sequences to model motion. Models include graph neural networks that treat the skeleton as a graph, and transformer or recurrent architectures for sequence modeling. Common datasets include COCO and MPII for 2D pose, NTU RGB+D and Human3.6M for 3D pose, used for pose-based tasks.

Applications: PoseBased enables gesture control, sign language recognition, action and activity recognition, sports analytics, virtual avatars for VR/AR, and rehabilitation or ergonomic assessment. It also informs animation and robotic control where human motion needs to be imitated.

Challenges: Occlusion, clutter, and varying lighting degrade pose estimates; 3D pose estimation from monocular data remains ill-posed. Real-time performance, generalization across individuals and activities, and privacy considerations are ongoing concerns. Multi-camera setups and sensor fusion can mitigate some issues.

Research landscape: Since the early 2010s, PoseBased approaches have evolved from 2D landmark detectors to end-to-end pipelines incorporating temporal and spatial reasoning, with improvements in accuracy and robustness. Emerging directions include 3D pose estimation, multi-modal sensing, and privacy-preserving representations.

See also: Pose estimation; Action recognition; Human-computer interaction; Motion capture.