本研究提出數位視訊目標人物搜尋機制，此機制假設使用者被提供一個包含目標人物的範例視訊，以及一個時間可能較長的待測視訊。範例視訊首先經過前處理，將代表多個視訊片段的人物畫面顯示於介面供使用者選取。使用者點選同個目標人物不盡相同之各式樣貌畫面後，本機制經選圖程序挑出若干目標人物影像建立目標樣板，並根據樣本於待測視訊中搜尋與標記可能包含目標人物的視訊片段。我們冀望透過這樣的機制協助建立以圖找圖之視訊中人物搜尋相關應用，例如視訊人物比對、檢索或錄影蒐證等。 本機制主要藉由人臉追蹤方法尋找視訊中包含人臉的連續畫面，利用多張影像的取得建立較穩定之目標人物樣板，再發展可靠的人臉評分方式以選擇較佳的人臉影像方便之後的識別與偵測，避免採用品質不佳的圖片影響運作，也可以減少相關的時間耗費。人臉評分方式主要考量人臉角度、銳利度、光線明暗與離鏡頭遠近等因素，鑒於人臉角度對於識別上的影響甚鉅，我們藉由雙邊濾波判斷人臉是否過度偏斜，實驗結果顯示我們所提出機制的準確度與未來可能的應用暨改進方向。 ;This research presents a targeted person searching scheme in digital videos. It is assumed that a user is given an exemplar video containing a person to be searched and a video, from which the scenes related the targeted person will be extracted. First, the exemplar video will be processed to select multiple representative images of persons, which will be shown on a user interface for the user to select the images of a targeted person. After choosing the images which best characterize the targeted person, the scheme will apply the face assessment process to build the model of the targeted person. The model can be employed to search that person in other videos. We hope that, by the assistance of such a scheme, searching people in videos can be facilitated. Such applications as actor comparison in videos, retrieval of people, or digital evidence collection can be achieved.
The scheme mainly relies on the face tracking method to find consecutive pictures or frames that contain human faces. With the acquirement of multiple images, we can build a more stable model of the targeted person and further develop a reliable face assessment method to choose better images for recognition. The assessment process not only avoids the images with poor quality, but also reduces operating time and efforts. The face assessment method takes four factors into consideration, including out-of-plane rotation, sharpness, brightness, and resolution. By analyzing parameters and recognition outcomes, we can understand the effects of different settings and interface influence, and investigate the utility of all aspects for face matching in videos. Experimental results show the accuracy of the proposed scheme and the possible improvement in the future.
