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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
dc.contributor.author | Kevin Lin | en |
dc.contributor.author | 林可昀 | zh_TW |
dc.date.accessioned | 2021-06-16T06:38:25Z | - |
dc.date.available | 2016-08-01 | |
dc.date.copyright | 2014-08-01 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-30 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57224 | - |
dc.description.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。 | zh_TW |
dc.description.abstract | 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. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T06:38:25Z (GMT). No. of bitstreams: 1 ntu-103-R01944012-1.pdf: 13465037 bytes, checksum: 0ddb74dbe22e8caec23e798f4b47e3de (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 口試委員會審定書 i
致謝ii 中文摘要iii Abstract iv Contents v List of Figures vii List of Tables x 1 Introduction 1 1.1 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Temporal Dual-rates Foreground Integration Method 4 2.1 Review of Background Modeling and Learning Rates . . . . . . . . . . . 4 2.2 Long-term and Short-term Background Modeling . . . . . . . . . . . . . 6 2.3 Foreground Extraction using the Complimentary Background Model . . . 8 2.4 Static Foreground Detection via Pixel-based Finite State Machine (PFSM) 10 3 Back-Tracing Verification 14 3.1 Pedestrian Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Owner Identification using the Back-Tracing Verification . . . . . . . . . 18 3.3 Object Abandoned Event Analysis . . . . . . . . . . . . . . . . . . . . . 23 4 Experimental Results 24 4.1 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2 Results on PETS2006 and AVSS2007 . . . . . . . . . . . . . . . . . . . 26 4.2.1 PETS2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2.2 AVSS2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Effectiveness of PFSM and Back-tracing Verification . . . . . . . . . . . 29 4.4 Realistic Environment Detection in Our Own Sequence . . . . . . . . . . 30 5 Conclusion 34 References 35 | |
dc.language.iso | en | |
dc.title | 監控視訊中偵測遺留物之研究 | zh_TW |
dc.title | Abandoned Luggage Detection for Visual Surveillance | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 陳祝嵩(Chu-Song Chen) | |
dc.contributor.oralexamcommittee | 王傑智(Chieh-Chih Wang),葉梅珍(Mei-Chen Yeh),蘇柏齊(Po-Chyi Su) | |
dc.subject.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, | zh_TW |
dc.subject.keyword | Abandoned object detection,Static foreground detection,Pixel-based finite state machine,Owner identification,Pedestrian detection, | en |
dc.relation.page | 38 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-07-30 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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