System flowchart
Our result
CAMShift wrong tracking result
Confusion matrix of recognizing grade rate
ABSTRACT:
For beginners, basketball dribbling training requires instructions from coaches. To ensure the training is effective, coaches usually teach details and emphases of each move and correct mistakes right away as a bystander. Then, they might just need training videos to practice autonomously, but the correctness of moves is not able to confirm objectively. Therefore, we present a Kinect-based assistance system for user to train autonomously at home or community centers no matter when. Moreover, our system is able to provide visual feedback of each move in real-time to ensure the correctness. We can get user’s skeleton information by using Kinect and Kinect skeletal tracking algorithm. We can also automatically detect basketball and get its location in real-time by improving existing object detect and tracking algorithm with color, depth, and body index images obtained from Kinect. For posture recognition, we separated dribble moves into details and emphases, defined these as independent postures, and built posture database by using a machine learning based tool－Microsoft Visual Gesture Builder. By combining skeleton, ball locations, and posture recognition, we can achieve our goal. Our system solves problems such as no coach as bystander or no objective confirmation of correctness of moves. Note that our system does not aim to replace conventional role of coaches, but to allow players and coaches to have more time flexibility. In addition, our system can be applied to serve a basketball team. Coach decides training menu, emphases of moves, and passing standard for each player and gets reports after players finish their training. Therefore, coach can teach more players at once, and player can choose when he/she wants to train in a time limit.
SUMMARY (中文總結):
籃球運球的訓練對於初學者來說必須要由教練教導每一個動作的細節、重點並從旁即時糾正錯誤，才能正確、有效的學習；有一定基礎後可以看影片跟著做訓練，但是動作的正確性無法客觀的確認。因此我們提出一套結合Kinect的輔助系統，讓使用者進行籃球運球的自主訓練時能有更大時間彈性，並在訓練中提供即時回饋以及訓練結束時的評分，以確保訓練的成效。
使用Kinect以及其骨架追蹤技術可以得到使用者的骨架資訊。而改良現有物件偵測與追蹤方法後我們可以由Kinect取得的影像中即時且自動地偵測與追蹤籃球。另外在姿勢辨識部分，利用Microsoft提供的工具Visual Gesture Builder（簡稱VGB）能將分解後動作細項自定義為各個獨立的姿勢並以機器學習的方法建立姿勢資料庫。我們結合骨架資訊、球的位置以及姿勢辨識來完成自主訓練輔助系統。
相較於以往請教練或自己看影片跟著做，我們的系統一樣能讓使用者知道哪部分需要改進，輔助使用者進行籃球運球自主訓練，解決需要教練陪同或沒有客觀檢查動作的問題。特別強調我們不是要取代傳統教練的角色，而是讓球員和教練都有更大的時間彈性，像是延伸應用到球隊上，教練可以決定訓練菜單的動作和分數的標準等等，訓練結束後收到球員們的訓練報告。讓球員能在一段時限內自己選擇時間完成訓練，也讓教練能一次教導更多的球員。
PROJECT MATERIAL:
- 計算運球次數
- 結果展示