發(fā)布時(shí)間：2018-05-20 16:20

  本文選題：智能引導(dǎo)系統(tǒng) + ARM�。� 參考：《東華大學(xué)》2010年碩士論文

【摘要】： 根據(jù)視頻統(tǒng)計(jì)現(xiàn)場人數(shù)并進(jìn)行自動引導(dǎo),是智能視頻監(jiān)控新的應(yīng)用趨勢。近年來,以上海世博會為代表的大型會展的興起為智能引導(dǎo)系統(tǒng)(IGS)的發(fā)展提供了契機(jī)。人們開始思考如何在嵌入式系統(tǒng)中運(yùn)用圖像處理技術(shù),實(shí)現(xiàn)人數(shù)的自動識別,進(jìn)而完成智能引導(dǎo),以提高參觀效率,保障游客安全。智能引導(dǎo)系統(tǒng)具有很高的商業(yè)價(jià)值和發(fā)展?jié)摿Α?本文綜合運(yùn)用嵌入式、圖像處理等技術(shù),從大型展廳的實(shí)際需求出發(fā),設(shè)計(jì)了基于ARM的人員智能引導(dǎo)系統(tǒng),采集現(xiàn)場圖像并實(shí)現(xiàn)人員自動計(jì)數(shù),系統(tǒng)分為硬件和識別算法兩部分。 系統(tǒng)的硬件選取EmbedSky公司的ARM9開發(fā)板TQ2440作為開發(fā)平臺,采用CMOS圖像傳感器OV9650采集現(xiàn)場圖像。在Linux環(huán)境下編寫攝像頭驅(qū)動程序,實(shí)現(xiàn)圖像采集,并將采集后的圖像生成BMP位圖文件。 對采集的位圖圖像通過梯形低通濾波器,調(diào)整閥值以去除大部分高斯噪聲。引入信噪比(SNR)作為判斷參數(shù),判定樣本圖像是否需要去噪,以減少運(yùn)算量;對圖像中因相對運(yùn)動而產(chǎn)生的運(yùn)動模糊,使用投影恢復(fù)法予以消除;采用背景差法提取前景目標(biāo),提出背景自適應(yīng)算法更新現(xiàn)場背景,抑制現(xiàn)場光照變化對目標(biāo)檢測的影響。 使用Canny邊緣檢測算法尋找邊緣,提取輪廓。依據(jù)圖像形態(tài)學(xué)理論,反復(fù)進(jìn)行腐蝕、膨脹運(yùn)算,填充封閉的空白區(qū)域,消除人員遮擋和粘連帶來的誤判,去除與人員無關(guān)的微小點(diǎn),形成較為完整的封閉輪廓;基于Hu矩不變量,構(gòu)造了基于物體輪廓線的輪廓矩不變量,它具備平移、旋轉(zhuǎn)和尺度不變性,獨(dú)立于物體本身的灰度。通過分析模板圖像的輪廓矩不變量,設(shè)定允許誤差的閾值,與現(xiàn)場提取的樣本圖像的輪廓矩進(jìn)行匹配,判斷人員數(shù)量,結(jié)合展廳容量以及報(bào)警設(shè)定值實(shí)現(xiàn)自動引導(dǎo)。 識別算法在Visual C++下,使用OpenCV編寫,調(diào)試通過后編寫Makefile文件,移植到Linux系統(tǒng)。 實(shí)驗(yàn)結(jié)果表明,在光線充足且背景相對簡單的場景中識別準(zhǔn)確率較高,當(dāng)光線減弱或者人員與背景紋理相近時(shí),識別準(zhǔn)確率出現(xiàn)下降。
[Abstract]:It is a new application trend of intelligent video surveillance to count the number of people in the field and conduct automatic guidance according to the video. In recent years, the rise of large-scale exhibition, represented by Shanghai World Expo, has provided an opportunity for the development of Intelligent guidance system (IGS). People begin to think about how to use image processing technology in embedded system to realize the automatic recognition of the number of people, and then complete the intelligent guidance, in order to improve the visit efficiency and ensure the safety of tourists. Intelligent guidance system has high commercial value and development potential. According to the actual demand of the large exhibition hall, this paper designs an intelligent personnel guidance system based on ARM, which can collect the scene images and realize the automatic counting of the personnel. The system is divided into two parts: hardware and recognition algorithm. The hardware of the system selects the ARM9 development board TQ2440 of EmbedSky company as the development platform, and adopts the CMOS image sensor OV9650 to collect the field image. In the environment of Linux, the camera driver is written to realize the image acquisition, and the captured image is generated into the BMP bitmap file. Through trapezoidal low-pass filter, the threshold is adjusted to remove most of the Gao Si noise. SNR (SNR) is introduced as the judging parameter to determine whether the sample image needs denoising in order to reduce the amount of computation; to eliminate the motion blur caused by relative motion in the image, the projection restoration method is used to remove it; and the background difference method is used to extract the foreground target. A background adaptive algorithm is proposed to update the scene background to suppress the effect of the field illumination change on the target detection. Canny edge detection algorithm is used to find the edge and extract the contour. According to the theory of image morphology, repeated corrosion, expansion operation, filling the closed blank area, eliminating the personnel occlusion and adhesion caused by misjudgment, removing the tiny points unrelated to the personnel, forming a relatively complete closed contour; Based on Hu moment invariant, the contour moment invariant based on object contour is constructed. It has the invariance of translation, rotation and scale, and is independent of the gray level of the object itself. By analyzing the invariant of the contour moment of the template image, setting the threshold of the allowable error, matching the contour moment of the sample image taken from the field, judging the number of the personnel, combining the capacity of the exhibition hall and the alarm setting value to realize the automatic guidance. The recognition algorithm is written with OpenCV under Visual C, and then the Makefile file is compiled after debugging, and then transplanted to Linux system. The experimental results show that the recognition accuracy is higher in the scene with sufficient light and relatively simple background. When the light is weakened or the person is close to the background texture, the recognition accuracy decreases.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2010
【分類號】：TP273.5

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 蘇曉倩;孫韶媛;戈曼;譙帥;谷小婧;;車載紅外圖像的行人檢測與跟蹤技術(shù)[J];激光與紅外;2012年08期

相關(guān)碩士學(xué)位論文 前1條

1 薛子伯;基于WiFi的觸發(fā)式無線圖像采集系統(tǒng)的研究與設(shè)計(jì)[D];吉林大學(xué);2011年

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本文編號：1915376