適用於先進監視系統之使用模型化場景精靈之視訊切割演算法及硬體架構設計

監視系統可以幫助人們監控生活環境，進而讓生活更為安全。而先進監視系統必須加入以內容為基礎的視訊處理技術，使得監視系統能夠具有智慧型的功能。先進監視系統之發展目標為自動化以及能夠大規模的監控。要達成自動化的目標，監視系統中必須有視訊切割的功能。視訊切割之物件外型遮罩結果，可以成為物件追蹤或是行為分析演算法之輸入資訊。要能夠進行大規模監控，某些運算必須被分散至攝影機端，以減少伺服器之運算負荷。由於視訊切割對於先進監視系統而言是非常必要的，並且對於不同的使用環境而言，視訊切割的演算法差異不大，我們認為視訊切割是非常適合被整合到攝影機上的。在本論文中，我們提出了使用模型化場景精靈之視訊切割演算法。此演算法目標監視攝影機，特別是旋轉式攝影機。我們利用了監視系統之特性來簡化場景精靈產生的過程。監視攝影機的特性有固定位置以及可預知的移動模式。本演算法即利用此二特性來進行簡化。對於固定位置旋轉的攝影機，可先將其拍攝到的畫面進行圓柱彎曲而轉換至圓柱座標系中。對於圓柱彎曲後的影像，全域移動估計(GME)所使用的運動模型參數可以由六個大幅減少至一或二個，而達到簡化場景精靈產生的目標。而利用場景精靈來當作視訊切割中的背景資訊，在偵測物件時會產生一些問題。我們對此提出了可調式閾限值以及場景精靈更新機制。可調式閾限值可以根據當下的畫面情況而動態調整閾限值；而場景精靈更新機制可以不斷的更新場景精靈，來產生最佳的視訊切割結果。同時，我們也針對了本演算法設計了其相對應的硬體架構。由於演算法設計時的目標是整合到監視攝影機中，因此我們希望設計低成本的硬體。我們在系統架構上提出了綜合管線架構，可有效減少畫面暫存記憶體的需求。另外，我們同時提出了模型化全域移動估計模組。在此模組中，我們利用Slice Buffer來解決由於不規則記憶體存取所造成的暫存空間過大的問題。此模型化移動估計模組也經由CIC，利用TSMC 0.18 μm製程下線進行驗證。晶片的大小為2.08×2.08 mm2，其處理能力為每秒120張QVGA或是30張VGA之畫面。

關鍵字

視訊切割；監視系統；場景精靈；全域移動估計；模型化

並列摘要

Video surveillance systems can help people to monitor environment and provide people more security of life. In advanced surveillance systems, content-based video processing techniques must be added to equip the surveillance systems with intelligent functions. The goals of advanced surveillance system are to become automatic and large in scale. To become automatic, video segmentation should be included into surveillance systems. The result object masks can be used as input for object tracking and behavior analysis algorithms. To become large in scale, some computations should be distributed to cameras to reduce computational load on the server. Since video segmentation is necessary and independent of application situations, we think that it is very suitable to be integrated into cameras. In this thesis, the algorithm of video segmentation using model-based sprite generation is proposed. This algorithm targets at surveillance cameras, especially panning cameras. We utilize the application characteristics to simplify the sprite generation process. That is, a surveillance camera usually has fixed position and known moving pattern, which are both considered in the proposed algorithm. For cameras with fixed position and panning motion, the captured frame can be first mapped to cylindrical coordinate by cylindrical warping. After cylindrical warping, the camera motion model of global motion estimation (GME) operation can be reduced from four or six parameters to only one parameter. With sprite as the background for segmentation, several object detection problems also occur, and we also proposed an adaptive threshold and sprite updating scheme. Adaptive threshold adjusts threshold value according to the current frame situation, where sprite updating scheme can constantly update sprite to generate better segmentation results. Corresponding architecture for video segmentation using model-based sprite generation is also proposed. Since this algorithm is designed to be integrated into cameras, the main hardware design goal is low cost. Mixed pipeline scheme is used to reduce buffer requirement for the whole system. In addition, hardware architecture design of model-based GME is also proposed, where we proposed slice buffer to solve the buffer wasting problem due to irregular memory access. The model-based GME is implemented with TSMC 0.18 μm 1P6M technology. The chip size is 2.08×2.08 mm2. This chip is capable of processing 120 QVGA frames or 30 VGA frames in one second.

並列關鍵字

video segmentation ； surveillance system ； sprite ； global motion estimation ； model-based

參考文獻

[2] V. Bhaskaran and K. Konstantinides, Image and Video Compression Standards:

[4] Text of ISO/IEC 15938-3/FCD Information technology - Multimedia content

[5] C. Regazzoni, V. Ramesh, and G. L. Foresti, “Special issue on video communications,

processing, and understanding for third generation surveillance

[6] J. C. Choi, S.-W. Lee, and S.-D. Kim, “Spatio-temporal video segmentation

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適用於先進監視系統之使用模型化場景精靈之視訊切割演算法及硬體架構設計

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