A Standing Long Jump Evaluation System Based on Silhouette Thinning and Dynamic Bayesian Networks
| Autor: | Yao-Bao Yen, 閻耀保 |
|---|---|
| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 From the research of sport experts, body controlling and development of a person’s muscle are related to coordinative training when he/she was a child. If there is any pose which is not good enough, it may influence his body controlling attributes in the future. We can observe this problem from the progress of standing long jump. But it takes a lot of time to see if each child’s pose is good enough or not when there are many children. So we want to develop a system to recognize the defined wrong poses while the human is doing standing long jump, and to give him suggestions about the wrong poses. This thesis can be divided into four parts. First of all, extract the silhouette of the user from each frame. There are 4 steps in the first part. Firstly, we have to re-build the background from the input image sequence. Secondly, the foreground object is extracted from each frame by subtracting the background from each frame. Thirdly, to remove noise from the result that was generated in the second step. The last step is to fill the holes of the extracted silhouette. The second part is to find out the skeleton of the extracted silhouette. We use the thinning algorithm and the graph theorem to complete this work. Using the basic graph operations based on the graph theorem to refine the raw skeleton generated by the thinning algorithm to remove noise. There are also four steps of this part. First step is to thin the input silhouette to obtain raw skeleton. Secondly, raw skeleton should be converted into a graph structure, and the structure of the graph is refined by removing the adjacent junction vertices. Then, remove the loops of the graph to make sure that a simple path between any two vertices is the only single path always. Fourthly, the redundant branches are pruned to obtain the final skeleton. The third part is judging the pose of the skeleton. By constructing and training the Dynamic Bayesian Network. We can then use this network to recognize the skeleton, finding out the most possible pose of each frame. There are three steps of this part. First, convert each skeleton into a feature vector. Then, train the Bayesian network with the feature vectors obtained in the previous step. Finally, we test the feature vectors converted from the test data by finding out the maximum probability of each feature vector in the trained network. The last part is to output suggestions for detected poses that are incorrect. If there is any pose recognized in previous part defined as “incorrect”, we will show the experts’ suggestion for this pose. This actually helps users to know how to adjust the pose just like there is a coach standing aside. The contribution of this research is the user actually can improve his poses by using this system. Although, we define only four different “incorrect” poses to give suggestions. In the future, we will add more information (e.g. more reference nodes, more precise equation of finding out the reference nodes, and more partitions) to this system to obtain better results. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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