公共場所人群加速度異常檢測系統(tǒng)
發(fā)布時間：2018-10-05 15:12
【摘要】：針對目前基于速度檢測公共場所密集人群異常行為存在的檢測準(zhǔn)確率低、使用范圍局限的問題,從人群的加速度角度對可能導(dǎo)致公共安全事故的人群異常行為進(jìn)行研究,提出了一種基于加速度檢測人群異常行為的算法,并基于該算法實現(xiàn)了針對人群逃散、人群聚集、人群擁擠和人群逆行4種異常行為檢測的系統(tǒng)。首先,利用金字塔Lucas-Kanade光流法進(jìn)行特征點跟蹤;然后,在獲取到特征點的速度矩陣基礎(chǔ)上進(jìn)一步計算其加速度矩陣,反映速度的整體變化;最后,從加速度大小和方向兩方面檢測人群異常行為。結(jié)果表明,所提算法檢測用時較少,相比基于速度檢測的對比算法,檢測的正確率提高到80%,誤報率降低為5%。
[Abstract]:In view of the problem of low detection accuracy and limited range of use in detecting abnormal behavior of dense crowd in public places based on speed measurement, this paper studies the abnormal behavior of people who may cause public safety accidents from the point of view of crowd acceleration. An algorithm based on acceleration is proposed to detect abnormal behavior of crowd. Based on this algorithm, four detection systems of abnormal behavior such as escaping, crowd gathering, crowd crowding and crowd retrograde are implemented. Firstly, the pyramid Lucas-Kanade optical flow method is used to track the feature points. Then, the acceleration matrix is further calculated on the basis of obtaining the velocity matrix of the feature points to reflect the global variation of the velocity. The abnormal behavior of the crowd was detected in terms of acceleration magnitude and direction. The results show that the detection time of the proposed algorithm is less, compared with the contrast algorithm based on speed detection, the accuracy of the detection is increased to 80 and the false alarm rate is reduced to 5 percent.
【作者單位】： 天津財經(jīng)大學(xué)理工學(xué)院;
【基金】：國家自然科學(xué)基金項目(61502331) 天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計劃項目(15JCQNJC00800) 中國民航信息技術(shù)科研基地開放課題(CAAC-ITRB-201504)
【分類號】：C913
[Abstract]:In view of the problem of low detection accuracy and limited range of use in detecting abnormal behavior of dense crowd in public places based on speed measurement, this paper studies the abnormal behavior of people who may cause public safety accidents from the point of view of crowd acceleration. An algorithm based on acceleration is proposed to detect abnormal behavior of crowd. Based on this algorithm, four detection systems of abnormal behavior such as escaping, crowd gathering, crowd crowding and crowd retrograde are implemented. Firstly, the pyramid Lucas-Kanade optical flow method is used to track the feature points. Then, the acceleration matrix is further calculated on the basis of obtaining the velocity matrix of the feature points to reflect the global variation of the velocity. The abnormal behavior of the crowd was detected in terms of acceleration magnitude and direction. The results show that the detection time of the proposed algorithm is less, compared with the contrast algorithm based on speed detection, the accuracy of the detection is increased to 80 and the false alarm rate is reduced to 5 percent.
【作者單位】： 天津財經(jīng)大學(xué)理工學(xué)院;
【基金】：國家自然科學(xué)基金項目(61502331) 天津市應(yīng)用基礎(chǔ)與前沿技術(shù)研究計劃項目(15JCQNJC00800) 中國民航信息技術(shù)科研基地開放課題(CAAC-ITRB-201504)
【分類號】：C913
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