中国畜牧杂志

[1] Bergman T J, Beehner J C. Information ecology:an integrative framework for studying animal behavior[J]. Trends Ecol Evol,2023, 38(11):1041-1050.

[2] Chen C, Zhu W, Norton T. Behaviour recognition of pigs and cattle:journey from computer vision to deep learning[J].Comput Electron Agric, 2021, 187:106255.

[3] Zhang Y J, Luo Z, Sun Y, et al. From beasts to bytes:revolutionizing zoological research with artificial intelligence[J]. Zool Res, 2023, 44(6):1115-1131.

[4] Siradjuddin I A, Sakinah A, Sophan M K. Combination of feature engineering and feature learning approaches for classification on visual complexity images[J]. J Info Int, 2021,17(3):991-1005.

[5] Garcia R, Aguilar J, Toro M, et al. A systematic literature review on the use of machine learning in precision livestock farming[J]. Comput Electron Agric, 2020, 179:105826.

[6]林克正,张元铭,李昊天.信息熵加权的HOG特征提取算法研究[J].计算机工程与应用, 2020, 56(6):147-152.

[7] Jaddoa M, Gonzalez L, Cuthbertson, H, et al. Multi view face detection in cattle using infrared thermography[A]. Applied computing to support industry:innovation and technology[C].Switzerland:Springer Cham, 2020:223-236.

[8]白云翔.基于改进HOG特征的斑马鱼多目标追踪[D].天津:南开大学, 2019.

[9]宋琳,刘永涛.加权HOG与特征融合行为识别方法研究[J].软件导刊, 2021, 20(11):53-56.

[10] Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[A]. Proceedings of 12th International Conference on Pattern Recognition[C]. New York:IEEE, 1994, 1:582-585.

[11] Meena K, Suruliandi A. Local binary patterns and its variants for face recognition[A]. 2011 International Conference on Recent Trends in Information Technology[C]. New York:IEEE,2011:782-786.

[12]张满囤,米娜,于洋,等.基于特征融合的奶牛个体识别[J].江苏农业科学, 2018, 46(24):278-281.

[13]朱瑞金,张涛,扎西顿珠.基于LBP特征的BP神经网络鱼群摄食行为检测研究[J].黑龙江科学, 2021, 12(18):5-8.

[14] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. Int J Comput Vision, 2004, 60(2):91-110.

[15] Tharwat A, Gaber T, Hassanien A E, et al. A Fully-automated zebra animal identification approach based on SIFT features[A]. Advances in intelligent systems and computing[C]. Switzerland:Springer Cham, 2017, 536:289-297.

[16] Awad A I, Zawbaa H M, Mahmoud H A, et al. A robust cattle identification scheme using muzzle print images[A]. 2013Federated Conference on Computer Science and Information Systems[C]. New York:IEEE, 2013:529-534.

[17]马驰原.基于多特征融合的船舶目标跟踪算法[D].大连:大连海事大学, 2022.

[18]肖文卿,汪鸿浩,詹长安.基于小波系数特征融合的小鼠癫痫脑电分类[J].计算机工程与应用, 2019, 55(14):155-161.

[19]李亿杨,孙龙清,孙鑫鑫.基于多特征融合的粒子滤波生猪采食行为跟踪[J].农业工程学报, 2017, 33(S1):246-252.

[20]张雯雯,徐杨,白芮,等.基于改进堆叠沙漏网络的动物姿态估计[J].计算机工程, 2023, 49(2):263-270.

[21] Utthara G, Suranjana S, Sukhendu D. A survey of decision fusion and feature fusion strategies for pattern classification[J].IETE Tech Rev, 2010, 27(4):293-307.

[22] Ahmed M, Seraj R, Islam S M S. The K-means algorithm:a comprehensive survey and performance evaluation[J]. Electronics, 2020, 9(8):1-12.

[23]李光昌,刘飞飞,李嘉豪.基于改进OpenPose算法的猪只行为识别方法[J].河南农业大学学报, 2022, 56(3):460-470.

[24] Tian F, Wang J, Xiong B, et al. Real-time behavioral recognition in dairy cows based on geomagnetism and acceleration information[J]. IEEE Access, 2021, 9:109497-109509.

[25] Yue S, Li P, Hao P. SVM classification:Its contents and challenges[J]. Appl Math Ser B, 2003, 18:332-342.

[26]何炜婷,曾碧.一种基于深度学习和SVM的动物姿态行为估计与心情识别方法:CN110457999A[P]. 2019-11-15.

[27]赵珊珊.基于行为识别的鹿个体疾病早期诊断研究[D].长春:吉林农业大学, 2021.

[28] Jena M, Dehuri S. Decisiontree for classification and regression:a state-of-the art review[J]. Informatica, 2020, 44(4):405-420.

[29] Kotsiantis S B. Decision trees:a recent overview[J]. Artif Intell Rev, 2013, 39:261-283.

[30] Tamura T, Okubo Y, Deguchi Y, et al. Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers[J]. Anim Sci J, 2019, 90(4):589-596.

[31] Khanh P, Chinh N, Cham T, et al. Classification of cow behavior using 3-DOF accelerometer and decision tree algorithm[A].2016 International Conference on Biomedical Engineering[C].New York:IEEE, 2016:45-50.

[32]陈紫城.基于时空兴趣点的生猪行为识别[D].广州:华南农业大学, 2016.

[33]肖夏伟.基于时空特征的猪饮水行为识别和特征融合的身份识别[D].镇江:江苏大学, 2022.

[34]庄晓霖.基于机器视觉的家禽异常行为检测方法研究[D].广州:华南农业大学,2019.

[35]赫磊,邵展鹏,张剑华,等.基于深度学习的行为识别算法综述[J].计算机科学, 2020, 47(S1):139-147.

[36] Feichtenhofer C, Pinz A, Zisserman A. Convolutional twostream network fusion for video action recognition[A].Proceedings of the IEEE conference on computer vision and pattern recognition[C]. New York:IEEE, 2016:1933-1941.

[37] Zhang K, Li D, Huang J, et al. Automated video behavior recognition of pigs using two-stream convolutional networks[J].Sensors, 2020, 20(4):1085-1085.

[38] Hao W, Zhang K, Zhang L, et al. TSML:A new pig behavior recognition method based on two-stream mutual learning network[J]. Sensors, 2023, 23(11):5092-5092.

[39] Shi L, Zhang Y, Cheng J, et al. Non-local graph convolutional networks for skeleton-based action recognition[J]. arXiv, 2018,1(2):3.

[40] Yin X, Wu D, Shang Y, et al. Using an EfficientNet-LSTM for the recognition of single cow’s motion behaviours in a complicated environment[J]. Comput Electron Agric, 2020, 177:105707.

[41] Qiao Y, Su D, Kong H, et al. Individual cattle identification using a deep learning based framework[J]. IFAC-Papers OnLine, 2019, 52(30):318-323.

[42] Chen C, Zhu W, Steibel J, et al. Recognition of aggressive episodes of pigs based on convolutional neural network and long short-term memory[J]. Comput Electron Agric, 2020, 169:105166.

[43] Wu D, Wu Q, Yin X, et al. Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector[J]. Biosyst Eng, 2020, 189:150-163.

[44] Li Z, Song L, Duan Y, et al. Basic motion behaviour recognition of dairy cows based on skeleton and hybrid convolution algorithms[J]. Comput Electron Agric, 2022, 196:106889.

[45] Wang Y, Li R, Wang Z, et al. E3D:an efficient 3d CNN for the recognition of dairy cow's basic motion behavior[J]. Comput Electron Agric, 2023, 205:107607.

[46] Qiao Y, Guo Y, Yu K, et al. C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming[J]. Comput Electron Agric, 2022, 193:106650.

[47]杜妍茹.基于计算机视觉的牛日常行为识别研究[D].包头:内蒙古科技大学, 2022.

[48]徐文凯.基于图像的水下动物目标识别与行为分析研究[D].青岛:青岛农业大学, 2022.

[49] Tu S, Zeng Q, Liang Y, et al. Automated behavior recognition and tracking of group-housed pigs with an improved DeepSORT method[J]. Agriculture, 2022, 12(11):1907.

[50]张宏鸣,武杰,李永恒,等.多目标肉牛进食行为识别方法研究[J].农业机械学报, 2020, 51(10):259-267.

[51]翟惠莹.基于RGBD和三维姿态特征融合的小鼠行为识别和分析[D].秦皇岛:燕山大学, 2022.

[52]李怡.基于多模型融合的奶山羊行为识别算法研究[D].杨凌:西北农林科技大学, 2021.

[53]欧士路.基于深度学习的湖羊行为识别研究与应用[D].扬州:扬州大学, 2022.

[54]史怀,陈峥,潘志针.基于机器视觉的猪个体身份和行为识别[J].福建畜牧兽医, 2021, 43(1):27-30.

[55]张飞宇,王美丽,王正超.引入Transformer和尺度融合的动物骨骼关键点检测模型构[J].农业工程学报, 2021, 37(23):179-185.