關鍵點偵測在電腦視覺領域中是相當重要的，其功能用於識別和定位圖像中的重要點，應用範圍涵蓋人類和動物姿勢估計，以及手部關鍵點偵測等領域。近年來，深度學習模型在這些領域展現了極高的準確率，然而，訓練這些模型需要大量的標註數據，而這些數據需要花費時間及精力的手動標註來完成。因此，半自動標註方法受到了重視。在我們的工作中，我們提出了一種基於跟踪器的半自動數據標註框架。我們使用人工標記物作為跟踪器的特徵，以實現更精確的跟踪。在獲取所有標記物坐標後，我們使用一個修補模型來移除這些人工標記物。通過我們的方法，我們節省了大量的數據標註時間。
耳穴針灸是中醫的重要治療之一。在這項研究中，我們將耳穴點視為關鍵點來展示我們提出的方法。我們訓練了一個關鍵點檢測模型用自動定位耳穴點，並達到了1.33毫米的估計誤差。

Keypoint detection is crucial in computer vision for identifying and localizing significant points in images, with applications ranging from human and animal pose estimation to hand keypoint detection. Recently, deep learning models for keypoint detection have demonstrated high accuracy across these domains. However, training such models requires extensive annotated data, predominantly achieved through labor-intensive manual annotation. Hence, semi-automatic annotation methods have gained traction. In our work, we proposed a semi-automatic data annotation frame which is tracking-based method. We used artificial markers as features for tracker to track more precisely. After retrieving all the coordinates of markers, we used an inpainting model to remove these artificial markers. By our method, we saved a lot of time for data annotation.
Ear acupuncture stands as one of the key treatments in Traditional Chinese Medicine (TCM). In this research, we regarded ear acupoints as keypoints to demonstrate our proposed approach. We trained a keypoint detection model which can automatically localize the ear acupoints and achieved an estimation error of 1.33 mm.

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