TABLE 1.
Provides a comparative overview of depth cameras and pose estimation frameworks, reviewing their typical error margins, envir onmental constraints, reliability, and suitability for recording players’ performance, with a focus on body limbs, setup complexity, and suitability for recording.
| System | Type | Error range | Environment suitability | Depth range (m) | 2D/3d output | Reliability (limb Focus) | Setup complexity | Key strengths and limitations |
|---|---|---|---|---|---|---|---|---|
| Kinect v2 | Depth Camera | ∼10°–20° (Pfister et al., 2014) | Indoor only | 0.5–4.5 (Kurillo et al., 2022) | 3D | Both (higher in the sagittal plane) | Low | Built-in skeletal tracking |
| ZED 2i | Stereo Camera | ∼5°–15° (Aharony et al., 2024) | Indoor/Outdoor, >3 m | 0.2–20 (Aharony et al., 2024) | 3D | Lower limb (wide FOV applications) | Medium-High | Higher performance for wide views |
| RealSense | Depth Camera | ∼10°–20° (Pilla-Barroso et al., 2024) | Indoor/Outdoor | 0.2–10 (Aharony et al., 2024) | 2D/3D | Lower limb (slow gait, squat) | Medium | Valid for close-range tracking |
| OpenPose | Software | ∼5°–15° (Ino et al., 2024) | Indoor/Outdoor | Camera-limited | 2D (pseudo-3D) | Upper limb (fine movement detection) | Medium | Good multi-person support |
| MediaPipe | Software | ∼<10° (Pilla-Barroso et al., 2024) | Indoor/Outdoor | Camera-limited | 2D | Lower limb (squats, gait) | Low | Fastest runtime, low memory use, suitable for mobile apps |
| AlphaPose | Software | ∼15°–25° (Hulleck et al., 2023) | Indoor/Outdoor | Camera-limited | 2D | Upper limb (combat/sport gestures) | Medium | Higher RMSE in some studies |
| DensePose | Software | ∼10°–20° (Suzuki et al., 2024) | Indoor preferred | Camera-limited | 2D/3D mesh | Both (AR/VR mesh mapping) | High | Ideal for multi-person and VR/AR |
The numbers refer to the references mentioned in the text.