Abandoned Luggage Detection for Visual Surveillance
碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 102 === 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...
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| Format: | Others |
| Language: | en_US |
| Published: |
2014
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| Online Access: | http://ndltd.ncl.edu.tw/handle/23087990399441595117 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 102 === 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.
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