本文選題：幀間差分法 + 眾數(shù)聚類分析　； 參考：《計算機應(yīng)用研究》2016年10期

【摘要】：針對智能視頻監(jiān)控中快速、準(zhǔn)確地檢測和識別運動物體的問題,提出了一種依據(jù)運動物體特征速度來檢測識別動體以及解讀其語義含義的算法。該方法以相對幀間差分法為基礎(chǔ),通過對預(yù)處理后的二值斑塊圖像的標(biāo)記,計算斑塊的像素長度作為其特征速度,并依據(jù)斑塊特征速度的眾數(shù)進(jìn)行聚類分析,從斑塊特征速度得到運動物體的特征速度語義解讀和運動物體的檢測識別。實驗結(jié)果表明,斑塊的特征速度不僅可以實現(xiàn)對運動物體的檢測,而且通過聚類分析可以準(zhǔn)確地得出動體特征的語義解讀。用特征速度和眾數(shù)聚類分析方法實現(xiàn)對運動物體的檢測識別和語義解讀,相對于其他統(tǒng)計算法簡單有效,便于智能攝像機的嵌入式開發(fā)。
[Abstract]:To solve the problem of fast and accurate detection and recognition of moving objects in intelligent video surveillance, an algorithm is proposed to detect and recognize moving objects and interpret their semantic meanings according to the characteristic velocity of moving objects. Based on the relative inter-frame difference method, the pixel length of the pre-processed binary patch image is calculated as the feature speed, and the clustering analysis is carried out according to the mode number of the plaque feature velocity. The semantic interpretation of feature velocity of moving object and the detection and recognition of moving object are obtained from the feature velocity of plaque. The experimental results show that the feature velocity of patches can not only detect moving objects, but also accurately interpret the semantic features of moving objects by cluster analysis. The method of feature speed and mode cluster analysis is used to detect and recognize moving objects and to interpret the semantics. Compared with other statistical algorithms, it is simple and effective, and is convenient for the embedded development of intelligent camera.
【作者單位】： 大連理工大學(xué)管理與經(jīng)濟學(xué)部;
【基金】：國家“十二五”資助項目子課題(2013BAK02B06-03)
【分類號】：TP391.41;TN948.6
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本文編號：1870348