傳統的保全監視系統只利用CCD攝影機和儲存裝置來顯示並紀錄畫面，他不但缺乏自動化的監控警告機制且需要耗費監控者大量的時間來處理繁雜的工作，在保全系統中加入自動化監控警告的機制將可大幅提升安全性且減少大量人力成本的支出。近年來由於恐怖攻擊行動在世界各地不斷發生，對於公共安全產生極大的威脅，因此在公共場合中對於可疑的人或物體的偵測研究近年來越來越受重視，本篇論文的目的即是在人潮擁擠的環境下找出可疑的人或被遺棄的物體，在本論文中，我們將以本質影像(intrinsic image)的想法為基礎在時間軸上找尋一序列的本質影像，另外我們將利用Motion filter和stable image的更新機制來過濾人潮並更新目前的背景以提高人潮擁擠環境中對於可疑物體判斷的正確性。此外我們將利用目前及過去一段時間內所得到的本質影像來建構短期及長期的偵測架構。經由實驗證明本論文在人潮擁擠的環境下可以正確且有效率的偵測到可疑物體，我們不僅在室內環境中得到良好的偵測效果，也在室外環境的實驗裡得到很好的驗證。

In traditional security surveillance systems, only CCD cameras and storage devices are equipped for the operators to monitor the surveillance environment. Thus, the watchmen have to handle lots of thorny tasks to take care of the security problem. It is generally considered that integrate automatic alarm mechanism into traditional surveillance system will contribute to the system reliability and save lots of labor works as well.
In this thesis, a novel approach was proposed to find suspicious people or objects within crowded environment. We formulate the detection system with the help of intrinsic-image-based background modeling, motion filtering, and alarm event validation. The intrinsic images which derive from consecutive frames were utilized to disclose the suspicious region within observed scenes. And the block-based motion filter incorporated with stable image updating strategy was applied to filter out the interference issue caused by large stream of moving pedestrians. Also, a block-based alarm method based on different examined period was employed to threshold the foreground objects within consecutive intrinsic images. The proposed method has been testified within various real-world video sequences and demonstrates its efficiency to disclose suspicious objects under crowded environment.

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