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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44062

Title: Abnormal Event Analysis Using Patching Matching and Concentric Features
Authors: Jun-Wei Hsieh
Sin-Yu Chen
Chao-Hong Chiang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Video surveillance
patch clustering
concentric features
scene clustering
Date: 2009-09
Issue Date: 2017-11-13T08:26:39Z
Publisher: 13th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Abstract: Abstract:This paper proposes a novel patch-based approach for abnormal event detection from a mobile camera using concentric features. It is very different from traditional methods which require the cameras being static for well foreground object detection. Two stages are included in this system i.e., training and detection, for scene representation and exceptional change detection of important objects like paintings or antiques. Firstly, at the training stage, a novel scene representation scheme is proposed for large-scale surveillance using a set of corners and key frames. Then, at the detection stage, a novel patch matching scheme is proposed for efficient scene searching and comparison. The scheme reduces the time complexity of matching not only from search space but also feature dimension in similarity matching. Thus, desired scenes can be obtained extremely fast. After that, a spider-web structure is proposed for missing object detection even though there are large camera movements between any two adjacent frames. Experimental results prove that our proposed system is efficient, robust, and superior in missing object detection and abnormal event analysis.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44062
Appears in Collections:[資訊工程學系] 演講及研討會

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