人體上半身姿態追蹤系統應用於移動式平台之人機互動

Abstract

在這篇論文中描述了一個利用單眼相機人體上半身姿態追蹤系統。由於人體模型使用高維度的狀態空間來表示，因此利用分割取樣(partitioned sampling)降低狀態的維度，再以粒子濾波器(particle filter)來估測狀態值，其中所使用的觀測資料為影像中時間上與空間上的資訊。當進行人機互動時，靜止的單眼相機無法由影像中得到足夠的資訊，因此可以利用移動相機平台至較好的觀測位置，得到足夠的資訊來修正系統的估測值。最後，透過實驗來驗證此系統的整體效能及可靠性。

This thesis presents an upper body tracking method with a monocular camera. The human model is defined in a high dimensional state space. We hereby propose a hierarchical structure model to solve the tracking problem by particle filter with partitioned sampling. The spatial and temporal information from the image is used to track the human body and estimate the human posture. When doing the human-robot interaction, a static monocular camera may not get plenty of information from the 2D images, so we must move the camera platform to a better position for acquiring more enriched image information. The proposed upper body tracking technique will then self-adjust to estimate the human posture during the camera movement. To validate the effectiveness of the proposed tracking approach, extensive experiments have been performed, of which the result appear to be quite promising.