mediapipe-pose-detection

MediaPipe pose detection loses tracking when detection or tracking confidence thresholds are set too high for your video quality. Lowering these thresholds allows the model to maintain landmark tracking even when visual certainty drops during rapid movements.

To fix landmark jitter in MediaPipe pose detection, you need to adjust the detection and tracking confidence thresholds. Tuning these parameters smooths the landmark tracking output and resolves fluctuating pose coordinates across video frames.

Left/right confusion in pose estimation occurs when the video input is rotated or frame dimensions are mismatched. Correcting video rotation properties and verifying frame dimensions before processing resolves mirrored or swapped body landmark coordinates.

To validate pose detection accuracy for jump analysis, compare the tracked knee landmarks against manual observation of the video frames. This validates that the estimated pose coordinates correctly align with the subject's actual physical movements.

Low confidence scores in MediaPipe landmark tracking are caused by poor lighting, occlusion, or fast movements that obscure body landmarks. Adjusting the detection and tracking confidence thresholds helps the model retain pose estimates under these suboptimal visual conditions.

Optimize MediaPipe pose detection performance by adjusting confidence thresholds and ensuring proper video rotation and frame dimension handling. These adjustments reduce processing overhead and improve landmark tracking stability across the video analysis pipeline.