視頻監(jiān)控系統(tǒng)下航站樓旅客異常行為檢測(cè)方法研究
發(fā)布時(shí)間：2018-11-18 21:11
【摘要】：機(jī)場(chǎng)航站樓作為民航交通和運(yùn)輸?shù)闹匾獦屑~之一,旅客吞吐量較高,屬于異常行為多發(fā)區(qū)域。而傳統(tǒng)的航站樓監(jiān)控系統(tǒng)因監(jiān)控點(diǎn)多且需人工監(jiān)測(cè)等特點(diǎn)顯得非常耗時(shí)費(fèi)力,已很難滿足機(jī)場(chǎng)安全管理的需要。因此,采用智能視頻分析技術(shù),主動(dòng)對(duì)航站樓內(nèi)旅客異常行為進(jìn)行實(shí)時(shí)檢測(cè)并報(bào)警,能有效協(xié)助機(jī)場(chǎng)安保人員處理異常事件,完善機(jī)場(chǎng)對(duì)突發(fā)事件的快速反應(yīng)能力。本文主要對(duì)智能監(jiān)控下旅客群聚、奔跑以及遺留物體三種異常行為的檢測(cè)方法所涉及的關(guān)鍵技術(shù)進(jìn)行深入研究,主要內(nèi)容有:首先,視頻監(jiān)控下運(yùn)動(dòng)目標(biāo)的提取。在傳統(tǒng)高斯混合模型的基礎(chǔ)上,優(yōu)化高斯模型的均值及方差的更新機(jī)制,引入HSV空間陰影去除方法,從而克服初始建模速度慢且存在大量陰影的缺點(diǎn),較好地重建視頻圖像的背景模型,實(shí)現(xiàn)運(yùn)動(dòng)目標(biāo)的提取。其次,旅客群聚和奔跑異常行為檢測(cè),根據(jù)這兩種異常行為在人群密度和運(yùn)動(dòng)特征上的表現(xiàn)形式的差異,提出各自的判斷指標(biāo)對(duì)異常行為進(jìn)行檢測(cè)。采用攝像機(jī)透視效應(yīng)的加權(quán)前景面積以及前景的二維聯(lián)合信息熵設(shè)計(jì)人群密度指標(biāo);利用金字塔LK光流特征計(jì)算能量和加權(quán)方向直方圖熵對(duì)運(yùn)動(dòng)特征進(jìn)行定量描述。通過(guò)不同模擬機(jī)場(chǎng)航站樓場(chǎng)景下的視頻序列進(jìn)行測(cè)試,對(duì)本文提出算法的可靠性進(jìn)行驗(yàn)證。最后,基于遺留物檢測(cè)的旅客異常行為檢測(cè)。采用不同更新速率的改進(jìn)GMM模型對(duì)場(chǎng)景的雙重背景進(jìn)行建模,去除行人等運(yùn)動(dòng)目標(biāo)的干擾,實(shí)現(xiàn)對(duì)場(chǎng)景中短期靜止目標(biāo)的提取;根據(jù)目標(biāo)的多個(gè)特征對(duì)其進(jìn)行跟蹤分析,當(dāng)其在場(chǎng)景中停留時(shí)間超過(guò)設(shè)定的閾值,則將其判斷為遺留物;結(jié)合遺留物的狀態(tài)變化和歷史圖像信息,判斷旅客是否為滯留物體或丟失物體的異常行為。通過(guò)不同模擬機(jī)場(chǎng)航站樓場(chǎng)景下的視頻序列進(jìn)行測(cè)試,驗(yàn)證本文算法的準(zhǔn)確性。
[Abstract]:As one of the important hubs of civil aviation traffic and transportation, airport terminal has a high passenger throughput and belongs to a region with frequent abnormal behavior. The traditional terminal monitoring system is very time-consuming and laborious because of the many monitoring points and the need of manual monitoring. It is difficult to meet the needs of airport security management. Therefore, using intelligent video analysis technology to detect and alarm the abnormal behavior of passengers in terminal building in real time can effectively assist airport security personnel to deal with abnormal events and improve the ability of airport to respond quickly to emergencies. In this paper, the key technologies involved in the detection of passenger clustering, running and residual objects under intelligent surveillance are studied. The main contents are as follows: first, the extraction of moving targets under video surveillance. On the basis of the traditional Gao Si mixed model, the updating mechanism of the mean and variance of Gao Si model is optimized, and the HSV space shadow removal method is introduced to overcome the shortcomings of slow initial modeling speed and a large number of shadows. The background model of video image is reconstructed well and the moving object is extracted. Secondly, the abnormal behavior of passenger clustering and running is detected. According to the difference of the two abnormal behaviors in the density and movement characteristics of the crowd, the paper puts forward their own judgment indexes to detect the abnormal behavior. The weighted foreground area of the camera perspective effect and the two-dimensional joint information entropy of the foreground are used to design the population density index, and the calculated energy and weighted direction histogram entropy of the pyramid LK optical flow feature are used to quantitatively describe the motion feature. The reliability of the proposed algorithm is verified by testing the video sequences in different simulated airport terminal scenarios. Finally, the passenger abnormal behavior detection based on the residue detection. An improved GMM model with different updating rates is used to model the dual background of the scene, to remove the interference of moving objects such as pedestrians, and to achieve the extraction of the short-term still targets in the scene. According to the multiple features of the target, the target is tracked and analyzed, and when the residence time in the scene exceeds the set threshold, the target is judged as a remnant. Combined with the state change of the remnant and the historical image information, the abnormal behavior of the passenger is judged whether the passenger is a stranded object or a lost object. The accuracy of this algorithm is verified by testing the video sequences of different simulated airport terminal scenarios.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;TN948.6
，
本文編號(hào)：2341251
[Abstract]:As one of the important hubs of civil aviation traffic and transportation, airport terminal has a high passenger throughput and belongs to a region with frequent abnormal behavior. The traditional terminal monitoring system is very time-consuming and laborious because of the many monitoring points and the need of manual monitoring. It is difficult to meet the needs of airport security management. Therefore, using intelligent video analysis technology to detect and alarm the abnormal behavior of passengers in terminal building in real time can effectively assist airport security personnel to deal with abnormal events and improve the ability of airport to respond quickly to emergencies. In this paper, the key technologies involved in the detection of passenger clustering, running and residual objects under intelligent surveillance are studied. The main contents are as follows: first, the extraction of moving targets under video surveillance. On the basis of the traditional Gao Si mixed model, the updating mechanism of the mean and variance of Gao Si model is optimized, and the HSV space shadow removal method is introduced to overcome the shortcomings of slow initial modeling speed and a large number of shadows. The background model of video image is reconstructed well and the moving object is extracted. Secondly, the abnormal behavior of passenger clustering and running is detected. According to the difference of the two abnormal behaviors in the density and movement characteristics of the crowd, the paper puts forward their own judgment indexes to detect the abnormal behavior. The weighted foreground area of the camera perspective effect and the two-dimensional joint information entropy of the foreground are used to design the population density index, and the calculated energy and weighted direction histogram entropy of the pyramid LK optical flow feature are used to quantitatively describe the motion feature. The reliability of the proposed algorithm is verified by testing the video sequences in different simulated airport terminal scenarios. Finally, the passenger abnormal behavior detection based on the residue detection. An improved GMM model with different updating rates is used to model the dual background of the scene, to remove the interference of moving objects such as pedestrians, and to achieve the extraction of the short-term still targets in the scene. According to the multiple features of the target, the target is tracked and analyzed, and when the residence time in the scene exceeds the set threshold, the target is judged as a remnant. Combined with the state change of the remnant and the historical image information, the abnormal behavior of the passenger is judged whether the passenger is a stranded object or a lost object. The accuracy of this algorithm is verified by testing the video sequences of different simulated airport terminal scenarios.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41;TN948.6
，
本文編號(hào)：2341251
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