本文選題：智能監(jiān)控　切入點：異常事件　出處：《華中科技大學(xué)》2007年碩士論文

【摘要】： 目前,大多數(shù)視頻監(jiān)視系統(tǒng)無法在無人值守情況下自動協(xié)助保安人員及時發(fā)現(xiàn)可疑或者異常的事件,被記錄下來的視頻一般被用作事后的證據(jù)。解決這個問題的方法之一就是采用智能視頻監(jiān)控系統(tǒng),它從這些大量視頻數(shù)據(jù)中檢測出含有可疑或者異常的行為,并及時地將這些行為報告給安全人員。異常事件檢測的實驗程序正是基于這一目標(biāo)而設(shè)計的。 實驗程序?qū)崿F(xiàn)對人的徘徊事件與群毆事件這兩類異常事件的檢測。在銀行、停車場等特殊場所,人的徘徊在一定程度上可以被認(rèn)為可疑的事件,這類事件主要表現(xiàn)為目標(biāo)的運動方向在一定的時間內(nèi)發(fā)生多次變化。因此,在獲得運動目標(biāo)軌跡的基礎(chǔ)上,通過分析其方向特征就可以確定人的徘徊是否屬于可疑事件。 由于群毆事件與其它事件在視覺上具有一定的可分性,理論上可以在非壓縮視頻中提取出能夠區(qū)分群毆事件與其它事件的運動特征。實驗程序中取每1秒內(nèi)的幀序列為一個小片段,對這些小片段依次提取運動強度、運動方向直方圖、行程長度、運動強度比例4類運動特征值,從這些片段的特征值中找出能夠區(qū)分群毆事件與其它事件的閾值,根據(jù)特征閾值即可識別出監(jiān)控視頻中是否含有群毆事件。 實驗研究發(fā)現(xiàn),基于運動軌跡分析的徘徊事件檢測方法具有實用性,其中,實現(xiàn)的基于感興趣區(qū)域的運動分割方法可以較好地提取出前景中的運動目標(biāo)。基于運動活動性的方法可以適用于場景復(fù)雜的情況下對群毆事件的檢測,并具有較高的查全率和查準(zhǔn)率。
[Abstract]:At present, most of the video surveillance system cannot unattended automatically assist the security personnel to detect suspicious or abnormal events, the recorded video data are used after evidence. One way to solve this problem is to use intelligent video surveillance system, it is from these massive video data can detect abnormal behavior, and timely report to the security personnel. The experimental procedure to detect abnormal events is designed based on this goal.
Experimental procedures to detect people wandering events and events of these two types of abnormal events. In the bank, a special place for parking lot, people wandering in a certain extent can be considered suspicious events, this event mainly direction of movement target vary within a certain period of time. Therefore, in based on the moving target, through the analysis of the characteristics of direction can be determined whether it belongs to the people around the suspicious events.
The brawl and other events have certain separability in vision, the theory can extract motion features in non compression can distinguish the brawl with other events in the video. The experimental procedure in sequence frames per seconds for a small fragment of these small fragments according to the extraction of exercise intensity, motion direction histogram, length of stroke, characteristic of the motion of 4 kinds of motion intensity ratio, to distinguish between events and find other events from the threshold characteristics of these fragments of value, the threshold value can be identified according to the characteristics of monitoring whether the video contains a free event.
Experiment results show that the detection method of trajectory analysis of wandering events based on the practical, the implementation method of ROI based motion segmentation can effectively extract moving objects in the foreground. Based on the method of motion activity detection can be used on brawl in the scene of complex situations, and has high the recall and precision.

【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2007
【分類號】：TP391.41

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 賈玉福;石堅;;無線多媒體傳感器網(wǎng)絡(luò)信息處理技術(shù)淺析[J];微計算機應(yīng)用;2010年09期

相關(guān)碩士學(xué)位論文 前1條

1 李曉東;基于監(jiān)控視頻的異常行為檢測技術(shù)研究[D];廣東工業(yè)大學(xué);2012年

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本文編號：1722896