計算機視覺領域中的多物體檢測和遮擋情形的跟踪是一個重要的研究課題，但大多數物體跟踪算法太過於複雜，並不適用於即時的跟踪系統。本文提出了一種擁有快速的處理速度的多物件跟踪方法，並且能夠有效的處理遮避情形。該方法主要改進了前景檢測系統，以獲得低複雜度和高質量的成果，並與其他背景減除技術進行比較，實驗圖表明，這種方法在計算速度和檢測率方面優於其他前景檢測方法。為了有效率的跟踪運動物件，我們使用物件標記的方法來消除雜訊和將運動物件進行分組。另外，還通過使用對象的軌跡預測和邊緣分析，提出了遮擋狀態下的三種處理情況:交錯情況，分離情況和單一標籤下有多物件的情況。利用這些方法，我們可以實現一套能穩定追蹤多個物件的系統，並且不用使用到物件的顏色資訊和外觀模型。最後，我們對前景切割演算法進行硬體架構的實現，在TSMC 90nm的製程下擁有150 MHz操作頻率，在這頻率下，能夠即時處理1080p的影像資訊，而我們也在FPGA (Altera Sockit)上進行驗證，擁有50MHz的操作速率，並且將處理結果透過VGA顯示在螢幕上。

Multi-object detection and occlusion tracking in the computer vision field is an important research topic, but most objects tracking algorithms are too complex and not practical for the real-time tracking system. This paper proposes a real-time occlusion-adaptive tracking method approach to resolving this issue. This method mainly improves the foreground detection to get low-complexity and high-quality effect. It also compares with other background subtraction techniques. Experimental figures show this method outperforms other foreground detection methods in terms of both computation speed and detection rate. For tracking moving objects, the proposed method uses the labeling to eliminate noises and group moving objects. In addition, it also proposed the processing cases of occlusions, including staggered case, separation case and multi-object in single label case, by using object's trajectory and edge. With this method, we can track the moving objects in the successive frame without color cues and appearance model in the real-time surveillance system. Finally, we implemented the hardware architecture of the foreground detection algorithm with a 150 MHz operating frequency at TSMC's 90 nm process. In this operating frequency, we can process 1080p image with 30 frames per second. And we are also working on the FPGA (Altera Sockit) for verifying. The results will be displayed on the screen through the VGA in 50MHz operating rate.

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