Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7483
標題: 利用可移動攝影機來實現以模糊系統為基礎之即時人臉追縱
Fuzzy System-based Real-Time Face Tracking With Pan-Tilt-Zoom Camera
作者: 張書瑋
Chang, Shu-Wew
關鍵字: Face Tracking;人臉追蹤
出版社: 電機工程學系所
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摘要: 
這篇論文提出一個利用可移動CCD攝影機來實現使用模糊系統為基礎的即時人臉追蹤系統。在人臉偵測中是根據使用HSV彩色空間中的色度(H)和濃度(S)和亮度(V)資訊來偵測,再利用偵測結果來做追縱。在人臉偵測方面我們以基於支持向量學習之TS型式自我組織模糊網路(SOTFN-SV)在彩色HS空間中做膚色分割。且為了減少明亮度的影響,我們設計一個根據每張圖的彩色空間亮度(V)資訊來調整每張圖適合的門檻值的模糊系統。膚色分割是為了偵測出適當的膚色區域來當人臉候選區域。接下來利用形態濾波器和以SOTFN-SV網路為基礎的人臉彩色資訊濾波器來偵測出較適合的人臉候選區域並減少錯誤偵測的個數。在人臉追蹤時,用卡門濾波器來平滑我們的追縱曲線和預估我們偵測的區域以便降低所需要偵測的時間。當多人同時出現在追蹤區域中時要為了要追蹤特定的人選,將利用到特定人選的衣服色彩資訊。在偵測的環境中當我們所要追蹤的對象背對鏡頭或偵測不到人臉時,為了避免追蹤失敗,於是利用連續兩張不同的影像資訊來尋找移動的物體來讓我們保持追蹤。在本篇論文上的人臉偵測方法的效能和其他時即的人臉偵測方法做比較的結果可以證實我們的人臉偵測方法可以達到較好的偵測效果。在即時追蹤中我們將控制攝影機的上下左右移動以便移動中的人可以保持在畫面中。為了使得人臉的大小適合我們持續的做追蹤的動作,我們也控制攝影機焦距縮放的功能來使得影像中的人臉能維持適中大小。在不同的實驗環境中,可得不錯的即時追蹤結果。

This thesis proposes a real-time face tracking system with a pan-tilt-zoom camera using the technique of fuzzy systems. Tracking is based on detected faces in the HSV (Hue-Saturation-Value) color space. To detect faces, a Self-Organizing TS-type Fuzzy Network with Support Vector learning (SOTFN-SV) is proposed to segment skin colors in the HS color space. To reduce the influence of illumination, a fuzzy system is designed to adaptively determine the SOTFN-SV segmentation threshold according the V color space in each image. Detected skin regions are considered as face candidates. A shape filter followed by a SOTFN-SV based facial color filter is employed to the detected face candidates to exclude components from the face candidates and reduce the number of false alarms. For face tracking, a Kalman filter algorithm is employed for face trajectory correction and prediction. The prediction function helps to narrow down the face detection region so that the detection time can be reduced. To track a specific person when there are multiple persons appearing in the detection region, the dressing color information of each detected person is used. In the circumstance that the tracked person is back to the camera and no face is detected, the consecutive frame difference is employed to track the moving person to avoid tracking lost. Performance of the proposed face detection method is compared to other real-time face detection methods and the results show that the proposed method achieves a better detection result. In real-time operation, the panning and tilting operation in camera is employed to keep the moving person located in the captured image, and the zooming operation is employed to keep a suitable detection face in the image. Experiments on different environments show good real-time face tracking results.
URI: http://hdl.handle.net/11455/7483
其他識別: U0005-1307200716164000
Appears in Collections:電機工程學系所

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