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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57224
Title: | 監控視訊中偵測遺留物之研究 Abandoned Luggage Detection for Visual Surveillance |
Authors: | Kevin Lin 林可昀 |
Advisor: | 洪一平(Yi-Ping Hung) |
Keyword: | 遺留物偵測,靜態前景偵測,基於像素的有限狀態機,物主驗證,行人偵測, Abandoned object detection,Static foreground detection,Pixel-based finite state machine,Owner identification,Pedestrian detection, |
Publication Year : | 2014 |
Degree: | 碩士 |
Abstract: | 本研究提出一個遺留物偵測方法,能夠有效偵測出監控環境中被放置的遺留物。
我們提出藉由結合 short-term 與 long-term 背景學習模型,對影像中的像素進行編碼與分類。同時,我們為影像中每一個像素建立一個有限狀態機,分析該像素的狀態轉換與變化過程,進而決定該像素是否屬於靜止不動的前景。為了完整分析遺留物的事件,我們追朔過去一段時間內的移動物體軌跡,分析並驗證嫌疑犯是否確實遠離了遺留物,並不再回來。 我們所提出的方法在兩個公開測試資料庫(PETS2006和 AVSS2007)獲得穩定、有效的偵測結果,並在偵測數據上勝過其他相關研究。 This thesis presents an effective approach for detecting abandoned luggage in surveillance videos. We combine short- and long-term background models to extract foreground objects, where each pixel in an input image is classified as a 2-bit code. Subsequently, we introduce a finite-state machine framework to identify static foreground regions based on the temporal transition of code patterns, and to determine whether the candidate regions contain abandoned objects by analyzing the back-traced trajectories of luggage owners. The experimental results obtained based on video images from 2006 Performance Evaluation of Tracking and Surveillance (PETS2006) and 2007 Advanced Video and Signal-based Surveillance (AVSS2007) databases show that the proposed approach is effective for detecting abandoned luggage, and that it outperforms previous methods. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57224 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊網路與多媒體研究所 |
Files in This Item:
File | Size | Format | |
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ntu-103-1.pdf Restricted Access | 13.15 MB | Adobe PDF |
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