Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose With Multiple Video Cameras
Bibliographic Data
| ID | 4615221 |
|---|---|
| Authors | Nobuyasu Nakano (0000-0001-5057-2136), Tetsuro Sakura, Kazuhiro Ueda (0000-0003-0030-0882), Leon Omura, Arata Kimura (0000-0002-8641-6220), Yoichi Iino (0000-0003-4132-838X), Senshi Fukashiro (0000-0001-9700-5390), Shinsuke Yoshioka (0000-0002-8945-9461) |
| Year | 2020 |
| Volume | 2 |
| Pages | 50-50 |
| Publication date | 2020-05-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2020.00050 |
| PMID | 33345042 |
| OpenAlex | W3031044065 |
| Language | EN |
| Citations received | 10 |
| References cited | 14 |
There is a need within human movement sciences for a markerless motion capture system, which is easy to use and sufficiently accurate to evaluate motor performance. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), and these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were 40 mm. The primary reason for mean absolute errors exceeding 40 mm was that OpenPose failed to track the participant's pose in 2D images owing to failures, such as recognition of an object as a human body segment or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30 mm or less
Computer graphics (images · Computer vision · Motion (physics · Motion Capture · Advanced Vision and Imaging · Computer Science · Human Pose and Action Recognition · Video Surveillance and Tracking Methods · Artificial Intelligence
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| Citations per year | 1.67 |
|---|---|
| Citation span | 2020 - 2024 (5) |
| Highly cited | No |