kneedetection

Knee detection refers to the process of locating the knee joint in images or video frames as part of human pose estimation. It can be performed in two dimensions, providing x and y coordinates or a confidence heatmap, or in three dimensions when depth information or multi-view data is available. Detecting the knee is a building block for understanding leg pose and gait.

Approaches: Traditional computer vision used feature detectors and model-based fitting; modern methods rely on deep learning. In 2D pose estimation, networks predict per-pixel or per-joint heatmaps for keypoints, from which knee coordinates are extracted. The top-down approach first detects persons and then regresses joints; the bottom-up approach detects all body joints then assembles them into person instances. 3D knee pose can be inferred from monocular cues, multi-view setups, or calibrated depth sensors.

Datasets and evaluation: Datasets such as MPII Human Pose, COCO, Leeds Sports Pose (LSP), and Human3.6M provide knee annotations. Evaluation uses metrics such as PCK (percentage of correct keypoints) with a threshold, AP based on the OKS, or 3D mean per-joint position error (MPJPE).

Applications and challenges: Knee detection is used in gait analysis, sports performance assessment, physical therapy, animation, and human-computer interaction. Challenges include occlusion, variation in clothing, rapid motion, viewpoint changes, and the need for temporal consistency in video.

Future directions: improving accuracy in occluded and crowded scenes, robust 3D inference from monocular video, real-time processing, and cross-domain generalization across populations and activities.