Name
Abstract | ||
|---|---|---|
In this work, we automatically distinguish the efficient high elbow pose from dropping one in pulling phase of front crawl stroke in front view amateurly recorded videos. This task is challenging due to the aquatic environment and missing depth information. We predict the pull's efficiency through multiclass svm and random forest classifiers given arms key positions and angles as the feature set. We evaluate our approach over a labeled dataset of video frames taken from 25 members of masters' swim club at Ryerson University with different levels of expertise and physiological characteristics. Our results show the effectiveness of our approach with random forest classifier, yielding 67% accuracy. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Swim Stroke Analysis, Pose Estimation |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Task analysis,Front crawl,Computer science,Support vector machine,Feature extraction,Pose,Feature set,Artificial intelligence,Random forest | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| hossein fani | 1 | 24 | 6.20 |
| Amin Mirlohi | 2 | 0 | 0.34 |
| Hawre Hosseini | 3 | 1 | 1.71 |
| Rainer Herpers | 4 | 73 | 19.86 |