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基于多特征融合相關濾波的運動奶牛目標提取
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國家重點研發(fā)計劃項目(2017YFD0701603)和陜西省重點產業(yè)創(chuàng)新鏈項目(2019ZDLNY02-05)


Target Extraction of Moving Cows Based on Multi-feature Fusion Correlation Filtering
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    摘要:

    為實現大規(guī)模養(yǎng)殖場內奶牛目標的自動提取,,將相關濾波算法融入目標提取基本框架,,提出一種相關濾波融合邊緣檢測的奶牛目標提取(Correlation filtering-edge detection based target extraction, CFED)算法,。首先利用顏色名(Color names, CN),、方向梯度直方圖 (Histogram of oriented gradient, HOG)設計的相關濾波器獲取奶牛目標范圍;再利用13個不同方向的邊緣濾波模板卷積目標范圍圖像得到圖像邊緣,,最后融合邊緣信息和顏色特征提取出奶牛目標,。對奶牛場不同環(huán)境下的9段視頻進行目標提取試驗,結果表明,,算法提取的目標與真實結果平均重疊率達到92.93%,,較Otsu、K-means聚類,、幀間差分法和高斯混合模型(Gaussian mixture model,,GMM)分別高35.63、32.84,、20.28,、14.35個百分點;平均假陽性率和假陰性率分別為5.07%和5.08%,,處理每幀圖像平均耗時0.70s,。該結果表明,提出的CFED算法具有較好的目標檢測能力,,為奶牛目標準確快速提取提供了一個有效方法,。

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    The accurate extraction of cow targets serves as the basis for the behavior analysis such as lameness detection, ruminate and estrus. In order to realize the automatic tracking and monitoring of dairy cows in large-scale farms, the correlation filtering algorithm was integrated into the basic framework of target extraction, and a cow target extraction algorithm (CFED) that combined correlation filtering and edge detection to extract the cow target was proposed. Firstly, the correlation filters constructed by the color names and the Histogram of oriented gradient were applied to obtain the cow target range box. Then 13 edge filter templates in different directions convolved the target image box to get the edge image. Finally, the edge information and color feature were combined to extract the cow target. In order to verify the effectiveness of CFED algorithm, experiments were conducted on nine pieces of video samples of moving cows under different environments and interferences. The results showed that the average overlap rate between the CFED results and the manually marked results reached 92.93%, which was 35.63 percentage points, 32.84 percentage points, 20.28 percentage points and 14.35 percentage points higher than that of Otsu, K-means clustering, frame difference method and Gaussian mixture model method, respectively. The false positive rate and false negative rate of CFED were 5.07% and 5.08%, respectively. The average time cost was 0.70s per frame. This result showed that the proposed CFED algorithm had good target detection ability in complex environments such as weather, scale and occlusion, which can provide an effective method for accurate and rapid extraction of dairy cow targets.

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秦立峰,張曉茜,董明星,岳帥.基于多特征融合相關濾波的運動奶牛目標提取[J].農業(yè)機械學報,2021,52(11):244-252. QIN Lifeng, ZHANG Xiaoqian, DONG Mingxing, YUE Shuai. Target Extraction of Moving Cows Based on Multi-feature Fusion Correlation Filtering[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(11):244-252.

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  • 收稿日期:2020-11-06
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  • 在線發(fā)布日期: 2021-11-10
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