【摘要】：隨著社會經(jīng)濟(jì)的不斷發(fā)展,人群密集的公共場所越來越多,如何對人群進(jìn)行有效的監(jiān)控已經(jīng)成為公共安全中的突出問題。智能視頻監(jiān)控采用計(jì)算機(jī)視覺、圖像處理和模式識別等技術(shù)對人群狀態(tài)進(jìn)行監(jiān)控,可以有效的檢測到人群中的異常行為。本文在研究分析群體異常行為檢測相關(guān)技術(shù)基礎(chǔ)上,設(shè)計(jì)了一種基于光流特征的群體異常行為檢測方法。本文首先對現(xiàn)有的運(yùn)動目標(biāo)檢測方法進(jìn)行了研究分析,在檢測運(yùn)動目標(biāo)時使用了混合高斯模型的方法,并對檢測到的目標(biāo)區(qū)域使用中值濾波器進(jìn)行平滑去噪處理,可以得到連通性和準(zhǔn)確度更為理想的運(yùn)動目標(biāo)區(qū)域。在對目標(biāo)特征進(jìn)行提取時,本文在研究分析Harris角點(diǎn)提取算法的基礎(chǔ)上,設(shè)計(jì)了一種改進(jìn)的多尺度Harris角點(diǎn)提取算法,使用該方法提取的特征點(diǎn)在數(shù)量上更加充足而且性質(zhì)更加穩(wěn)定。其次,在進(jìn)行群體異常檢測時,本文先使用金字塔Lucas-Kanade光流法對提取的特征點(diǎn)進(jìn)行跟蹤匹配,進(jìn)而可以得到群體的光流場,通過光流場中的光流矢量信息提取群體的運(yùn)動特征,本文提取的運(yùn)動特征包括運(yùn)動的平均動能和方向熵,將計(jì)算得到的運(yùn)動特征值與預(yù)先設(shè)定的閾值進(jìn)行比較來判斷群體是否存在異常,在進(jìn)行比較判斷時本文采用連續(xù)五幀的運(yùn)動特征值與閾值進(jìn)行比較,如果連續(xù)五幀的值都大于閾值,則判斷群體存在異常行為。最后,為了驗(yàn)證本文設(shè)計(jì)的群體異常檢測方法性能,通過對UMN數(shù)據(jù)集進(jìn)行實(shí)驗(yàn)測試,實(shí)驗(yàn)結(jié)果表明本文設(shè)計(jì)的群體異常檢測方法在準(zhǔn)確率和實(shí)時性方面都比較理想。
[Abstract]:With the continuous development of social economy, more and more crowded public places, how to effectively monitor the crowd has become a prominent problem in public security. Intelligent video surveillance uses computer vision, image processing and pattern recognition technology to monitor the state of the crowd, can effectively detect abnormal behavior in the crowd. In this paper, based on the research and analysis of the correlation technology of group abnormal behavior detection, a method based on optical flow characteristics is designed to detect group abnormal behavior. In this paper, the existing methods of moving target detection are studied and analyzed, and the mixed Gao Si model is used to detect the moving target, and the median filter is used to smooth the target region. A moving target region with better connectivity and accuracy can be obtained. On the basis of studying and analyzing the Harris corner extraction algorithm, an improved multi-scale Harris corner extraction algorithm is designed in this paper. The feature points extracted by this method are more abundant in quantity and more stable in nature. Secondly, in the process of group anomaly detection, the pyramidal Lucas-Kanade optical flow method is used to track and match the extracted feature points, and then the optical flow field of the group can be obtained, and the motion characteristics of the group can be extracted by the optical flow vector information in the optical flow field. The motion features extracted in this paper include the average kinetic energy and directional entropy of motion. The calculated motion eigenvalues are compared with the pre-set threshold to determine whether the population is abnormal or not. In this paper, the motion eigenvalues of five consecutive frames are compared with the threshold. If the values of the five consecutive frames are all larger than the threshold, the group has abnormal behavior. Finally, in order to verify the performance of the group anomaly detection method designed in this paper, the experimental results of UMN data set show that the proposed method is ideal in both accuracy and real-time.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TN948.6

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本文編號：2286405