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基于DiMP的類特定養(yǎng)殖奶山羊跟蹤方法
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陜西省農(nóng)業(yè)科技創(chuàng)新驅(qū)動項目 (NYKJ-2021-YL(XN)48)


Class-special Real-time Dairy Goat Tracking Method Based on DiMP
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    摘要:

    為準(zhǔn)確實時跟蹤羊只目標(biāo),進(jìn)行疾病異常預(yù)警,實現(xiàn)奶山羊精細(xì)化養(yǎng)殖,本文基于DiMP跟蹤模型,利用奶山羊跟蹤對象單一且圖像樣本豐富的特點,結(jié)合遷移學(xué)習(xí)和類特定融合方法,設(shè)計了一種類特定的奶山羊目標(biāo)跟蹤模型,能夠有效克服DiMP算法在跟蹤類特定目標(biāo)時定位精度不足的缺點。利用構(gòu)建的奶山羊視頻跟蹤數(shù)據(jù)訓(xùn)練集對跟蹤算法進(jìn)行遷移訓(xùn)練,加快模型收斂速度,使評估網(wǎng)絡(luò)預(yù)測出的邊界框更貼合奶山羊真實框的位置和尺寸。在線跟蹤階段,針對目標(biāo)模板僅采用第1幀特征制作整個序列的調(diào)制向量,導(dǎo)致該調(diào)制向量相對整個跟蹤階段特征不具代表性,與后續(xù)幀差異大的缺點,使用訓(xùn)練集制作包含奶山羊各種姿態(tài)的類調(diào)制向量,以指數(shù)消融方式更新奶山羊類調(diào)制向量與第1幀調(diào)制向量間的比重,增強邊界框回歸任務(wù)中的奶山羊特征與背景的判別性。提出的算法在測試集上的AUC(Area under curve)和精準(zhǔn)度(Precision)分別為76.20%和60.19%,比DiMP方法分別提升6.17、14.18個百分點,跟蹤速度為30f/s,滿足實時跟蹤的要求。實驗結(jié)果表明,提出的類特定奶山羊目標(biāo)跟蹤方法可用于監(jiān)測復(fù)雜場景下奶山羊的運動,為奶山羊精細(xì)化管理提供了技術(shù)支持。

    Abstract:

    In the process of fine breeding of dairy goats, the accurate and real-time tracking of goat targets is an important basis for their behavior recognition and disease abnormality warning. Based on the DiMP tracking model, a kind specific target tracking model was designed for dairy goats, which can effectively overcome the disadvantage of insufficient positioning accuracy of DiMP algorithm in tracking specific targets. The migration training of the tracking algorithm was carried out by using the constructed dairy goat video tracking data training set to accelerate the convergence speed of the model and make the boundary box predicted by the evaluation network more fit the position and size of the real frame of the dairy goat. In the online tracking stage, aiming at the disadvantage that the target template only used the first frame features to produce the modulation vector of the whole sequence, which led to unrepresentative characteristics of the modulation vector relative to the whole tracking stage, the training set was used to produce the class modulation vector containing various poses of the dairy goat, and the proportion between the class modulation vector of the dairy goat and the modulation vector of the first frame was updated by exponential ablation to enhance the discrimination between characteristics and background of dairy goats in the boundary box regression task. The AUC and accuracy of the proposed algorithm on the test set were 76.20% and 60.19%, respectively, which were 6.17 and 14.18 percentage points higher than that of the DiMP method. The tracking speed was 30 frames per second (f/s),which met the requirements of real-time tracking. The experimental results showed that the proposed target tracking method can be used to monitor the movement of milk goats in complex scenes, and it can provide technical support for fine management of dairy goats.

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寧紀(jì)鋒,張靜,楊蜀秦,胡沈榮,藍(lán)賢勇,王勇勝.基于DiMP的類特定養(yǎng)殖奶山羊跟蹤方法[J].農(nóng)業(yè)機械學(xué)報,2023,54(6):280-286,400. NING Jifeng, ZHANG Jing, YANG Shuqin, HU Shenrong, LAN Xianyong, WANG Yongsheng. Class-special Real-time Dairy Goat Tracking Method Based on DiMP[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(6):280-286,400.

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  • 收稿日期:2022-07-06
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  • 在線發(fā)布日期: 2022-08-16
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