發(fā)布時間：2018-03-25 01:09

  本文選題：奶牛行為　切入點：目標(biāo)分割　出處：《農(nóng)業(yè)機械學(xué)報》2017年06期

【摘要】：為從海量監(jiān)控視頻中快速、準(zhǔn)確識別影響奶牛繁殖與健康的行為,以小育成牛舍與泌乳牛舍中400頭奶牛為研究對象,分析了奶牛在活動區(qū)與奶廳匝道的運動行為,提出了一種基于圖像熵的奶牛目標(biāo)對象識別方法,通過最小包圍盒面積計算與目標(biāo)對象輪廓圖,實時捕獲奶牛爬跨行為與蹄部、背部特征,融合被識別奶牛連續(xù)7 d的運動量,判斷影響奶牛健康繁殖的異常行為。試驗結(jié)果表明,利用本文方法對監(jiān)控視頻內(nèi)奶牛目標(biāo)對象、運動行為進(jìn)行實時監(jiān)測,有效監(jiān)控識別奶牛發(fā)情、蹄病行為準(zhǔn)確率超過80%,發(fā)情漏檢率最低為3.28%,蹄病漏檢率最低為5.32%,提高了規(guī)�；B(yǎng)殖管理效率。
[Abstract]:In order to quickly and accurately identify the behaviors that affect the reproduction and health of dairy cows from mass surveillance video, 400 cows in small and lactating cattle houses were used as research objects, and the movement behavior of dairy cows in active areas and on the ramp of milk hall was analyzed. A method of dairy cow object recognition based on image entropy is proposed. By calculating the area of the minimum bounding box and the contour of the target object, the crawling behavior, hoof and back features of the cow are captured in real time, and the moving amount of the identified cow for 7 consecutive days is fused. The experimental results show that the method of this paper is used to monitor the object and movement behavior of dairy cattle in the surveillance video in real time, and to monitor and identify the estrus of dairy cow effectively. The accuracy rate of foot-and-foot disease was more than 80%, the lowest rate of estrus leakage was 3.28%, and the lowest rate of foot-and-foot disease rate was 5.32%, which improved the efficiency of large-scale culture management.
【作者單位】： 北京交通大學(xué)計算機與信息工程學(xué)院;國家農(nóng)業(yè)信息化工程技術(shù)研究中心;
【基金】：國家自然科學(xué)基金面上項目(61571051)
【分類號】：S823;TP391.41

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