Abandoned Luggage Detection for Visual Surveillance
Date Issued
2014
Date
2014
Author(s)
Lin, Kevin
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.
Subjects
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
Type
thesis
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