以人類為基礎的視訊處理及其在監控上的應用

Abstract

近年來以人類為基礎的視訊處裡一直是相當熱門的研究題目，主要原因為人類通常是影片中的拍攝對象，例如，電影，監視器影片，以及運動影片。因此，如果可以在視訊影片中針對人類的部分加以處理，對於視訊內容的分析會相當的有幫助。常見的人類為基礎的視訊處理包含了人類偵測，切割，以及動作辨識等技術。並且可分為針對儲存影片的off-line 處理以及針對即時環境的on-line 處理。在本論文中，我們提出以人類為基礎的視訊處理技術並且將其應用在智慧型監控系統上。在第一項的研究中，我們針對已經錄製完畢的影片，提出了以背景資訊為基礎的場景分割方法。我們利用Mosaic 的技術將屬於前景部分(通常是人物的部分)的資訊移除並且試著重建被遮蔽的背景。接著根據背景的資訊取出低階的視覺特徵來估測影片中兩shot 之間的相似程度。並且參考電影製作的法則將shot 群組化找出影片中不同場景之間的邊界位置。在找尋出場景邊界位置之後，可以簡化後續的視訊分析工作。在第二項的研究主題中，我們針對即時的監控系統提出我們的主動式攝影機網路動作規劃技術。主動式攝影機可以拉近來觀測目標，可以提供較清楚的影像，因此非常符合智慧型監控系統上的需求。因此，我們提出一個線性生產規劃賽局(Linear Production Game)解法來控制攝影機網路中主動式Pan Tilt Zoom 攝影機的參數。我們提出的非線性函式可以更加有效的攝影機去追蹤多個觀測目標，並且經由參數的拓展以及加上新的線性限制條件，可以轉換為一個線性生產規劃賽局(Linear Production Game)。由於線性生產規劃賽局可以在多項式時間內求得最佳解，因此我們提出的方法相當有效率以及精確。在第三項的研究主題中，我們針對人類動作辨識問題提出一個以局部特徵為基礎的辨識技術。我們根據局部特徵的表示法提出一個人類動作辨識的架構。兩種不同的局部特徵，包含動作的長期趨勢以及短期外型變化分別被抽取出來用來描述人類動作。最後經由adaboost 的學習方法取出具有鑑別力的局部特徵組合來辨識人類動作。

In recent years, human-based video processing has attracted a great deal of attention in the field of computer vision. This is because human usually is the major subject in a video such as movie, surveillance video, and sport video. Therefore, a videovprocessing technique based on human can provide rich information for video content analysis. Generally, common human-based video processing includes human detection, human segmentation, human motion recognition, and so on. Furthermore, according to the real-time requirements of an application, it can be categorized to the off-line processing for a video storage and the on-line processing for a real-time environment. In this dissertation, we put our emphasis on the human-based video processing and apply these techniques to an intelligent surveillance application. In the first topic, we propose a scene segmentation approach based on the analysis of background information for the off-line processing. The mosaic technique is utilized to remove the foreground parts (human) and reconstruct the occluded background. According to the background information, several low-level visual features are integrated to compute the similarity measure between two shots; moreover, the rules of film-making are used to guide the shot grouping process. After the boundaries among different scenes are detected, the following video analysis rocessing can be simplified. In the second topic, we proposed an active camera network reconfiguration technique for an on-line surveillance system. Since an active camera (for example, a pan, tilt, zoom camera) be able to fixate a human subject to obtain a large view of people, it is suitable for intelligent surveillance system. Therefore, a camera network reconfiguration solution is proposed to adjust pan, tilt, and zoom parameters in a PTZ camera network for video surveillance application. The non-linear objective function we proposed better utilizes a network's cameras to track multiple targets. We also show that, by expanding the unknown parameters and imposing new constraints, the non-linear objective function can be converted into a linear production game (LPG) problem. Since an LPG yields an optimal solution that can be evaluated in polynomial time, the proposed method is efficient and accurate. In our third work, a human motion recognition framework based on local feature representation is proposed. A clay based feature to describe long-term movement trend and a motion history image (MHI) based feature to describe short-term shape variation, are extracted respectively. Then, the AdaBoost approach is applied to select a best feature set for discriminating the human motions.