本文選題：視頻分析 + 目標(biāo)檢測(cè)和跟蹤�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：隨著城市化的快速發(fā)展,涌現(xiàn)出大量的高層建筑,極大依賴了電梯系統(tǒng),如何提高電梯的安全性和運(yùn)行效率已成為迫切需求。本文分析了當(dāng)前電梯存在的安全問(wèn)題,主要包括人員超載、打斗、放置危險(xiǎn)品等乘客異常狀態(tài),在不需要人為干預(yù)的情況下,使用監(jiān)控視頻信息分析電梯乘客狀態(tài),檢測(cè)出異常后進(jìn)行報(bào)警,具有較好的研究意義和工程應(yīng)用價(jià)值。本文研發(fā)了基于視頻的電梯人數(shù)統(tǒng)計(jì)算法、電梯人員異常行為檢測(cè)算法以及電梯遺留物檢測(cè)算法。采用基于VIBE背景建模算法,實(shí)現(xiàn)目標(biāo)檢測(cè);級(jí)聯(lián)Adaboost和SVM分類器,并輔以Hough圓變換進(jìn)行人頭識(shí)別;同時(shí)使用基于MedianFlow算法進(jìn)行跟蹤計(jì)數(shù),實(shí)現(xiàn)了電梯實(shí)時(shí)人數(shù)統(tǒng)計(jì)算法。采用運(yùn)動(dòng)估計(jì)提取運(yùn)動(dòng)信息,本文在搜索策略和匹配函數(shù)上改進(jìn)了經(jīng)典的三步運(yùn)動(dòng)搜索法;然后計(jì)算運(yùn)動(dòng)區(qū)域的熵值,并根據(jù)運(yùn)動(dòng)信息線索判斷是否出現(xiàn)異常行為,最后實(shí)現(xiàn)電梯人員異常行為檢測(cè)算法。采用基于VIBE的雙背景模型提取目標(biāo)前景,并對(duì)輸出圖像進(jìn)行形態(tài)學(xué)濾波和連通域分析;判斷物體短暫靜止,并在其主體離開(kāi)后進(jìn)行計(jì)時(shí),最后實(shí)現(xiàn)遺留物檢測(cè)算法。本文對(duì)基于視頻的電梯乘客異常狀態(tài)的三個(gè)檢測(cè)算法分別進(jìn)行了測(cè)試,并分析了測(cè)試結(jié)果,可準(zhǔn)確實(shí)時(shí)地統(tǒng)計(jì)乘客人數(shù)、檢測(cè)乘客異常行為以及檢測(cè)遺留物。
[Abstract]:With the rapid development of urbanization, a large number of high-rise buildings have emerged, greatly dependent on the elevator system, how to improve the security and operational efficiency of elevators has become an urgent need. This paper analyzes the current elevator safety problems, including personnel overload, fighting, placing dangerous goods and other passengers abnormal state, without the need for human intervention, the use of monitoring video information to analyze elevator passenger status, It has good research significance and engineering application value to alarm after detecting anomaly. In this paper, a video based algorithm of elevator population statistics, elevator personnel abnormal behavior detection algorithm and elevator residue detection algorithm is developed. The algorithm based on VIBE background modeling is used to realize target detection; cascaded Adaboost and SVM classifier, supplemented by Hough circle transform, are used to recognize human head; at the same time, based on MedianFlow algorithm to track and count, the elevator real-time number statistics algorithm is realized. Using motion estimation to extract motion information, this paper improves the classical three-step motion search method in search strategy and matching function, then calculates the entropy of motion region, and determines whether abnormal behavior occurs according to the clues of motion information. Finally, the algorithm of detecting the abnormal behavior of elevator personnel is realized. The dual-background model based on VIBE is used to extract the target foreground, and the output image is analyzed by morphological filtering and connected domain analysis. In this paper, three algorithms for detecting abnormal status of elevator passengers based on video are tested, and the test results are analyzed, which can accurately and real-time count the number of passengers, detect the abnormal behavior of passengers and detect the remnants.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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相關(guān)期刊論文 前1條

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本文編號(hào)：1966199