監控視訊中偵測遺留物之研究

Abandoned Luggage Detection for Visual Surveillance

林可昀(Kevin Lin)

指導教授 ： 洪一平

共同指導教授 ： 陳祝嵩(Chu-Song Chen)

國立臺灣大學/電機資訊學院/資訊網路與多媒體研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.03099

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

Abandoned object detection ； Static foreground detection ； Pixel-based finite state machine ； Owner identification ； Pedestrian detection

參考文獻

[1] Chris Stauffer and W Eric L Grimson. Adaptive background mixture models for real-time tracking. In Proc. CVPR, volume 2, 1999.

[2] Fatih Porikli, Yuri Ivanov, and Tetsuji Haga. Robust abandoned object detection using dual foregrounds. EURASIP Journal on Advances in Signal Processing, page 30, 2008.

[3] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv preprint arXiv:1311.2524, 2013.

[5] Ruben Heras Evangelio, Tobias Senst, and Thomas Sikora. Detection of static objects for the task of video surveillance. In Proc. WACV, pages 534–540, 2011.

[6] YingLi Tian, Rogerio Schmidt Feris, Haowei Liu, Arun Hampapur, and Ming-Ting Sun. Robust detection of abandoned and removed objects in complex surveillance videos. IEEE TSMC Part C, 41(5):565–576, 2011.