本文選題：航站樓集群安全 + 人數(shù)統(tǒng)計�。� 參考：《南京航空航天大學》2014年博士論文

【摘要】：隨著我國民航運輸業(yè)的快速發(fā)展,機場旅客數(shù)量也隨之劇增,由此導致的大量旅客群體性事件給機場的安全生產(chǎn)帶來了很大壓力。盡管現(xiàn)代機場都已裝備了視頻監(jiān)控系統(tǒng)以輔助管理者及時發(fā)現(xiàn)航站樓中的異常情況,但傳統(tǒng)的視頻監(jiān)控系統(tǒng)還停留在人工觀測的預警方式,極為耗費人力。隨著計算機數(shù)字圖像技術的不斷發(fā)展,新一代智能化視頻監(jiān)控系統(tǒng)正成為新的研究熱點。它能夠利用圖像處理和模式識別算法自主性地分析視頻數(shù)據(jù),并對其中發(fā)生的異常情況給出預警,從而減輕安全監(jiān)管過程中的人力消耗。為此,本文在深入分析國內(nèi)外智能視頻監(jiān)控技術研究現(xiàn)狀以及我國機場航站樓安全管理需求的基礎上,重點研究了航站樓集群安全識別與預警問題中所涉及的若干關鍵技術。首先,本文對智能視頻處理的基礎技術前景提取進行了研究,設計了一種改進的高斯混合背景模型用于前景提取。改進后的模型將傳統(tǒng)模型中的標準差用平均偏差代替,節(jié)約了計算量。同時,還通過引入HSV陰影濾除模型減少了陰影對前景提取的影響。此外,設計了一種歸一化前景面積計算方法,以解決前景面積這一傳統(tǒng)圖像特征在用于人數(shù)統(tǒng)計時由于透視效應的影響而無法準確估計人數(shù)的問題。其次,本文從人群密度估計的角度對旅客人數(shù)統(tǒng)計問題展開了研究,分別提出了三種不同類型的人數(shù)統(tǒng)計方法。第一種算法通過一個具有尺寸自適應特性的滑動窗和一組判別條件在對前景二值圖進行行人檢測掃描的基礎上實現(xiàn)了中低密度場景下的人數(shù)統(tǒng)計。第二種算法基于歸一化前景和角點信息特征設計一種遮擋因子,以克服遮擋對人數(shù)統(tǒng)計效果的影響,實現(xiàn)了中高人群密度場景下的人數(shù)統(tǒng)計。第三種算法基于單應性原理設計一種考慮了遮擋因素的目標匹配策略,以提升單視點場景中人數(shù)統(tǒng)計效果,實現(xiàn)了多視點條件下的人數(shù)統(tǒng)計。另外,針對航站樓集群安全管理中所關注的旅客異常行為問題,本文從行人異常行為識別角度,設計了兩種算法,分別用于識別旅客聚集行為和旅客肢體沖突行為。第一種算法在計算出歸一化前景面積和二維聯(lián)合熵的基礎上,設計了一種人群聚集檢測參數(shù),以實現(xiàn)對人群聚集行為的有效識別。第二種算法在計算出的前景區(qū)域光流信息基礎上,利用提取的光流方向熵特征,實現(xiàn)了對行人肢體沖突行為的有效識別。最后,本文設計了一種航站樓集群安全識別與預警原型系統(tǒng)。它能在獲取傳統(tǒng)視頻監(jiān)控系統(tǒng)中視頻數(shù)據(jù)的基礎上,利用相關圖像處理算法和并行處理技術,實現(xiàn)多路視頻條件下的人數(shù)統(tǒng)計和行人異常行為識別等功能,并進一步驗證了本文所設計的旅客人數(shù)統(tǒng)計和旅客異常行為識別算法的有效性。
[Abstract]:With the rapid development of China's civil aviation transportation industry, the number of airport passengers has also increased dramatically, resulting in a large number of passenger group incidents to bring great pressure to airport safety production. Although modern airports have been equipped with video surveillance systems to help managers discover the abnormal situation in the terminal building in time, the traditional video surveillance system still stays in the early warning mode of manual observation, which is extremely labor-intensive. With the development of computer digital image technology, a new generation of intelligent video surveillance system is becoming a new research hotspot. It can make use of image processing and pattern recognition algorithms to analyze video data autonomously, and give early warning of abnormal situation in it, thus reducing the manpower consumption in the process of security supervision. Therefore, on the basis of deeply analyzing the research status of intelligent video surveillance technology at home and abroad and the security management requirements of airport terminal building in China, this paper focuses on some key technologies involved in the identification and early warning of terminal cluster security. Firstly, this paper studies the basic technology of intelligent video processing, and designs an improved Gao Si hybrid background model for foreground extraction. The improved model replaces the standard deviation of the traditional model with the average deviation, which saves the calculation cost. At the same time, the influence of shadow on foreground extraction is reduced by introducing HSV shadow filtering model. In addition, a method of calculating normalized foreground area is designed to solve the problem that foreground area, a traditional image feature, can not be estimated accurately because of the influence of perspective effect. Secondly, from the point of view of crowd density estimation, this paper studies the problem of passenger statistics, and puts forward three different types of statistics methods. In the first algorithm, a sliding window with adaptive size and a set of discriminant conditions are used to realize the population statistics of low and low density scenes on the basis of the pedestrian detection scan of the foreground binary map. The second algorithm designs an occlusion factor based on normalized foreground and corner information features to overcome the effect of occlusion on the population statistics and realize the population statistics under the scenario of high population density. The third algorithm is based on the principle of homography to design a target matching strategy which takes into account occlusion factors to improve the effect of population statistics in single view scene and realize the population statistics under the condition of multiple viewpoints. In addition, aiming at the problem of abnormal behavior of passengers concerned in the cluster security management of terminal building, this paper designs two algorithms from the perspective of pedestrian abnormal behavior recognition, which are used to identify passenger aggregation behavior and passenger limb conflict behavior respectively. On the basis of computing normalized foreground area and two-dimensional joint entropy, the first algorithm designs a crowd aggregation detection parameter to realize the effective identification of crowd aggregation behavior. On the basis of the calculated optical flow information in foreground region, the second algorithm uses the extracted entropy feature of optical flow direction to realize the effective identification of pedestrian limb conflict behavior. Finally, a prototype system of terminal cluster security identification and early warning is designed. On the basis of obtaining video data in traditional video surveillance system, it can use correlation image processing algorithm and parallel processing technology to realize the functions of number statistics and pedestrian abnormal behavior identification under multi-channel video condition. Furthermore, the effectiveness of the proposed algorithm is verified.
【學位授予單位】：南京航空航天大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：V351.3 ;TN948.6

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本文編號：2043668