本文選題：全方位視覺 + ATM機(jī)智能監(jiān)控�。� 參考：《浙江工業(yè)大學(xué)》2009年碩士論文

【摘要】： 近年來(lái),自動(dòng)取款機(jī)(ATM)在各商業(yè)銀行大量投入使用,在方便用戶存取款的同時(shí),也帶來(lái)了越來(lái)越多的ATM機(jī)糾紛案件和金融犯罪,而目前的銀行系統(tǒng)由于缺乏智能化監(jiān)控手段導(dǎo)致此類犯罪案件持續(xù)上升。本文將全方位視覺傳感器引入ATM機(jī)智能監(jiān)控領(lǐng)域,克服了現(xiàn)有監(jiān)控系統(tǒng)監(jiān)控范圍小、智能化水平低、圖像信息融合困難等缺陷。 本文的主要工作和研究成果如下: 1.設(shè)計(jì)了一套基于全方位視覺傳感器的ATM機(jī)智能監(jiān)控裝置并用Java實(shí)現(xiàn)了一套完整的ATM機(jī)智能監(jiān)控系統(tǒng),該系統(tǒng)可以完成ATM環(huán)境中的徘徊滯留行為、窺視跨線行為、敲砸暴力行為和張貼虛假?gòu)V告行為的識(shí)別,實(shí)驗(yàn)證明該系統(tǒng)實(shí)時(shí)性和魯棒性都比較理想。 2.在前景目標(biāo)提取方面,針對(duì)所關(guān)心的不同對(duì)象研究了混合高斯算法、膚色人臉檢測(cè)算法、最大熵閾值分割算法和梯度紋理分割算法,在對(duì)上述算法詳細(xì)分析和研究的基礎(chǔ)上分別進(jìn)行了代碼實(shí)現(xiàn)。對(duì)混合高斯算法做了改進(jìn)以適應(yīng)ATM環(huán)境中用戶時(shí)停時(shí)走的情況,對(duì)不同色彩空間和膚色模型下的人臉提取效果做了實(shí)驗(yàn)比較。 3.在運(yùn)動(dòng)目標(biāo)跟蹤方面,分析了現(xiàn)有跟蹤算法的優(yōu)缺點(diǎn),對(duì)其中應(yīng)用廣泛的顏色跟蹤算法(CAMSHIFT)和交互式多模型跟蹤算法(IMM)做了深入研究和代碼實(shí)現(xiàn),對(duì)這兩種算法的跟蹤效果做了實(shí)驗(yàn)比較。 4.在高層行為語(yǔ)義理解方面,在分析了目前常見的行為理解方法及優(yōu)缺點(diǎn)的基礎(chǔ)上,將支持向量機(jī)(SVM)算法應(yīng)用于人體軌跡訓(xùn)練和徘徊滯留行為識(shí)別,將行為語(yǔ)義規(guī)則判定應(yīng)用于其他三種異常行為的識(shí)別。在用戶使用ATM機(jī)的同時(shí)抽取出用戶人臉圖像,以減少存儲(chǔ)負(fù)擔(dān)和方便后續(xù)的人臉識(shí)別等。
[Abstract]:In recent years, ATM (ATM) has been put into use in a large number of commercial banks. While it is convenient for users to deposit and withdraw money, it has also brought more and more ATMs dispute cases and financial crimes. The current banking system, due to the lack of intelligent monitoring means, such crimes continue to rise. In this paper, the omnidirectional vision sensor is introduced into the intelligent monitoring field of ATM, which overcomes the defects of the existing monitoring system, such as small monitoring range, low level of intelligence, difficulty in image information fusion and so on. The main work and research results of this paper are as follows: 1. A set of ATMs intelligent monitoring device based on omni-directional vision sensor is designed and a complete ATM intelligent monitoring system is implemented with Java. The system can realize hovering behavior in ATM environment and peep across lines. The experiments show that the real time and robustness of the system are ideal. 2. In the aspect of foreground target extraction, mixed Gao Si algorithm, skin color face detection algorithm, maximum entropy threshold segmentation algorithm and gradient texture segmentation algorithm are studied for different objects concerned. On the basis of the detailed analysis and research of the above algorithms, the code is implemented separately. The mixed Gao Si algorithm is improved to adapt to the situation of user time stoppage in ATM environment, and the effect of face extraction under different color space and skin color model is compared experimentally. 3. In the aspect of moving target tracking, the advantages and disadvantages of the existing tracking algorithms are analyzed. The color tracking algorithm (CAMS HIFT) and the interactive multi-model tracking algorithm (IMM), which are widely used, are studied and implemented in code. The tracking effect of these two algorithms is compared experimentally. 4. In the aspect of high-level behavior semantic understanding, based on the analysis of common behavior understanding methods and their advantages and disadvantages, support Vector Machine (SVM) algorithm is applied to human trajectory training and hovering behavior recognition. The behavior semantic rule decision is applied to the recognition of the other three abnormal behaviors. In order to reduce the storage burden and facilitate the subsequent face recognition, the user face images are extracted simultaneously by using ATM machine.
【學(xué)位授予單位】：浙江工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2009
【分類號(hào)】：TP277

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 孫軍;基于計(jì)算機(jī)視覺的人證同一性研究[D];浙江工業(yè)大學(xué);2012年

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本文編號(hào)：1790292