A Fuzzy Model-based Integration Framework for Vision-based Intelligent Surveillance Systems

被引：0
|
作者：
Wahyono [1 ]
Filonenko, Alexander [1 ]
Kurnianggoro, Laksono [1 ]
Jo, Kang-Hyun [1 ]
机构：
[1] Univ Ulsan, Sch Elect Engn, Ulsan 680749, South Korea
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS (ICM) | 2017年
基金：
新加坡国家研究基金会;
关键词：
vision-based intelligent surveillance system; cascade support vector machine; color probability model; fuzzy model; integration framework; FIRE DETECTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper addresses a framework for event decision of vision-based intelligent surveillance system based on the fuzzy model. The input probabilities of the tasks for the fuzzy system are computed using the object detector which combines a cascade support vector machine (SVM) and color probability model (CPM). The SVM is used for identifying either the human, vehicle or baggage, while the CPM is applied to detect any possible smoke and fire regions in the monitoring area. The tracking algorithm is also integrated for triggering an alarm of suspicious event. The effectiveness of the proposed framework is evaluated under several video sequences with the comprehensive scenario. The results show that the framework can be one of the solutions for developing intelligent surveillance system.
引用
收藏
页码：358 / 361
页数：4
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