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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44103

Title: Suspicious object detection using fuzzy-color histogram
Authors: Chi-Hung Chuang
Jun-Wei Hsieh
Luo-Wei Tsai
Pei-Shiuan Ju
Kao-Chin Fan
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Object detection
Histograms
Event detection
Humans
Hidden Markov models
Packaging
Motion detection
Video sequences
Surveillance
Shape
Date: 2008-05
Issue Date: 2017-11-14T08:49:30Z
Publisher: 2008 IEEE International Symposium on Circuits and Systems
Abstract: Abstract:This paper proposes a novel method to detect suspicious objects from videos for abnormal event analysis. When considering a robbery event happens, there should be some suspicious object transferring conditions following between the forager and the victim. Since there is no prior knowledge about the object’s property, it is difficult to automatically analyze the conditions without any manual efforts. To tackle this problem, a ratio histogram based on fuzzy c-means algorithm is proposed for finding suspicious objects. Furthermore, we use Gaussian mixture models to model the suspicious object’s visual properties so that it can be accurately segmented from videos. After analyzing its subsequent motion features, different abnormal events like robbery can be effectively detected from videos. Experiment results have proved that the proposed method is robust, accurate, and powerful in abnormal event detection.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44103
Appears in Collections:[資訊工程學系] 演講及研討會

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