本文選題：移動物體監(jiān)控　切入點：ARM11　出處：《西安建筑科技大學(xué)》2017年碩士論文

【摘要】：近年來,隨著社會的進步和科技的飛速發(fā)展,移動物體監(jiān)控技術(shù)也得到了迅速發(fā)展。移動物體監(jiān)控在保護人身安全和財產(chǎn)安全中發(fā)揮著越來越重要的作用。然而傳統(tǒng)的視頻監(jiān)控大多只是視頻的錄制,不能對移動物體進行提取,后續(xù)工作是需要人工進行實時觀看處理,效率過低且費時。本設(shè)計采用移動物體檢測技術(shù)和視頻監(jiān)控技術(shù)相結(jié)合的方法,實現(xiàn)了基于ARM11的移動物體監(jiān)控系統(tǒng)的設(shè)計�；趯ΡO(jiān)控的基本功能和性能的考慮,設(shè)計了聲音報警子系統(tǒng)和攝像頭開發(fā)子系統(tǒng),并完成了從web瀏覽器訪問web服務(wù)器中保存的移動物體圖片和視頻的設(shè)計。軟件設(shè)計的主要任務(wù)是對移動物體監(jiān)控系統(tǒng)提出一種新的移動物體檢測算法。首先分析了移動物體檢測的研究現(xiàn)狀、移動物體檢測算法和視頻監(jiān)控中存在的主要問題。在對時間平均法和像素估計法進行建模分析后,發(fā)現(xiàn)時間平均法得到的背景模型中存在虛影;像素估計法得到的背景圖像會將物體的倒影誤判為前景,出現(xiàn)噪聲導(dǎo)致檢測效果不準確。針對時間平均法和像素估計法的自身的不足,本文將兩種算法的優(yōu)勢進行融合,提出了一種改進像素估計法,使兩種算法達到了優(yōu)勢互補的效果,改進像素估計法得到的背景圖像相對穩(wěn)定,而且可以提取到理想的前景目標。具有較好的魯棒性和自適應(yīng)性,為移動物體監(jiān)控提出了一種有效的算法。最后對基于ARM11的移動物體監(jiān)控系統(tǒng)作了測試,測試結(jié)果表明該系統(tǒng)可以實現(xiàn)移動物體的檢測和報警,具有一定的實用價值。本文在移動物體監(jiān)控系統(tǒng)的設(shè)計中最大的創(chuàng)新點是針對移動物體檢測算法的創(chuàng)新,將時間平均法和像素估計法的優(yōu)勢相融合設(shè)計研究出了一種改進像素估計法,使得移動物體的檢測擺脫了虛影和噪聲的干擾,檢測效果達到了預(yù)想的結(jié)果,使得所設(shè)計的移動物體檢測系統(tǒng)能實現(xiàn)對監(jiān)控范圍內(nèi)移動物體圖像的采集功能并且具有較高的實用性和適應(yīng)性。
[Abstract]:In recent years, with the progress of society and the rapid development of science and technology, mobile object monitoring technology has also been rapidly developed.Mobile object monitoring plays a more and more important role in protecting personal safety and property safety.However, most of the traditional video surveillance is just video recording, can not extract moving objects, the follow-up work is to manually real-time viewing processing, inefficient and time-consuming.The design of mobile object monitoring system based on ARM11 is realized by the combination of mobile object detection technology and video surveillance technology.Based on the consideration of the basic function and performance of the monitoring, the sound alarm subsystem and the camera development subsystem are designed, and the design of accessing the pictures and videos of moving objects stored in the web server from the web browser is completed.The main task of software design is to propose a new mobile object detection algorithm for mobile object monitoring system.Firstly, the research status of mobile object detection, the main problems in mobile object detection algorithm and video surveillance are analyzed.After modeling and analyzing the time averaging method and the pixel estimation method, it is found that there is a virtual image in the background model obtained by the time averaging method, and the background image obtained by the pixel estimation method will misjudge the reflection of the object as the foreground.The presence of noise results in inaccurate detection results.In view of the shortcomings of the time averaging method and the pixel estimation method, this paper combines the advantages of the two algorithms, and puts forward an improved pixel estimation method, which makes the two algorithms achieve the effect of complementary advantages.The background image obtained by the improved pixel estimation method is relatively stable and can extract ideal foreground targets.It has good robustness and adaptability, and proposes an effective algorithm for moving object monitoring.Finally, the mobile object monitoring system based on ARM11 is tested. The test results show that the system can realize the detection and alarm of moving object, and it has certain practical value.In this paper, the biggest innovation in the design of mobile object monitoring system is the innovation of moving object detection algorithm. An improved pixel estimation method is developed by combining the advantages of the time averaging method and the pixel estimation method.The detection of moving objects gets rid of the interference of virtual image and noise, and the detection effect reaches the desired result.The designed mobile object detection system can realize the collection function of moving object image in the monitoring range and has higher practicability and adaptability.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP277

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本文編號：1715119