【摘要】：視頻監(jiān)控攝像機的廣泛使用和智能視頻監(jiān)控技術(shù)的發(fā)展帶動了視頻監(jiān)控市場的蓬勃發(fā)展,以人作為視頻監(jiān)控主體的監(jiān)控系統(tǒng)不再有能力實時處理由成百上千路攝像頭全天候輸入的海量監(jiān)控視頻。視頻異常事件檢測作為智能視頻監(jiān)控的重要分支,可以借助計算機視覺技術(shù),從監(jiān)控視頻中主動檢測出與大多數(shù)正常行為事件不相符合的少量異常行為事件,并及時發(fā)出報警信息,從而將傳統(tǒng)的人從坐在屏幕前監(jiān)控枯燥的工作中解脫出來。本文具體所做的工作有:1、分析A.Adam提出的基于觀察點的異常事件檢測算法原理及應(yīng)用優(yōu)缺點,針對其等間距觀察點布置可能造成的不同環(huán)境下監(jiān)控區(qū)域信息丟失及計算冗余,提出了基于場景的觀察點自組織方案,在等間距觀察點布置的基礎(chǔ)上,實現(xiàn)不同監(jiān)控場景觀察點位置和密度的自動調(diào)整,應(yīng)用性更強。2、基于SEED-DVS6446達芬奇開發(fā)板,實現(xiàn)了可運行在其DSP端的基于觀察點的異常事件檢測算法,以及ARM端的異常檢測系統(tǒng),最終形成了視頻異常事件檢測盒,接通電源后能夠?qū)尤氲囊曨l流實時檢測是否發(fā)生異常事件并確定異常區(qū)域范圍。3、使用混合高斯背景模型提取前景運動團塊,光流法計算團塊運動方向;分別采用“Hog+線性SVM”以及“Haar+級聯(lián)結(jié)構(gòu)AdaBoost”的方案在運動團塊圖像上進行行人和車輛檢測;對檢測到的行人或車輛采用團塊跟蹤獲得其在視頻場景中的運動軌跡。結(jié)合本文歸納的異常事件規(guī)則集,實現(xiàn)對監(jiān)控場景下如人車越界、人車拌線等行為可描述的具體異常事件判別。從理論到實踐,通過對前面三個部分內(nèi)容的集成,實現(xiàn)了完整的視頻異常事件管理系統(tǒng):可以將達芬奇平臺上基于觀察點算法的異常事件檢測系統(tǒng),以及基于運動目標檢測與跟蹤的具體異常事件判別結(jié)合起來,協(xié)同運行,能有效地對視頻場景中行人和車輛相關(guān)的已知和未知異常事件的實時檢測。
[Abstract]:The wide use of video surveillance cameras and the development of intelligent video surveillance technology have led to the vigorous development of the video surveillance market. The surveillance system, which uses human as the main body of video surveillance, no longer has the ability to process the massive surveillance video which is input by hundreds of cameras all the time in real time. As an important branch of intelligent video surveillance, video abnormal event detection can actively detect a small number of abnormal behavior events from monitoring video, which does not accord with most normal behavior events. And timely send out alarm message, thus freeing the traditional people from sitting in front of the screen monitoring boring work. The specific work of this paper is to analyze the principle and application advantages and disadvantages of the anomaly event detection algorithm proposed by A.Adam based on observation points, aiming at the information loss and computational redundancy of monitoring area in different environments caused by the arrangement of observation points at equal spacing. Based on the arrangement of observation points at equal distance, the self-organization scheme of observation points based on scene is proposed. The automatic adjustment of observation points' position and density in different monitoring scenes is realized, and the application is stronger. 2. Based on SEED-DVS6446 da Vinci development board, An outlier event detection algorithm based on observation point and an anomaly detection system based on ARM are implemented in the DSP terminal. Finally, the video anomaly event detection box is formed. After the power supply is turned on, the abnormal event can be detected in real time and the range of abnormal area. 3. The foreground motion block can be extracted by using mixed Gao Si background model, and the moving direction of the block can be calculated by optical flow method. The schemes of "Hog linear SVM" and "Haar cascaded structure AdaBoost" are used to detect pedestrian and vehicle on the motion cluster image, respectively, and the detected pedestrian or vehicle is tracked by cluster block to obtain their motion track in the video scene. Combined with the rule set of abnormal events summarized in this paper, we can distinguish the concrete abnormal events which can be described under the monitoring scenario such as the behavior of the human vehicle crossing the boundary, the human-vehicle mixing line, and so on. From theory to practice, through the integration of the first three parts, a complete video anomaly event management system is implemented: the anomaly event detection system based on the observation point algorithm can be used on the Da Vinci platform. By combining the detection of moving targets with the discrimination of specific abnormal events, the real-time detection of known and unknown abnormal events related to pedestrians and vehicles in the video scene can be effectively realized.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN948.6

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本文編號：2247680