1 |
ARCHER S C, GREEN M J, HUXLEY J N. Association between milk yield and serial locomotion score assessments in UK dairy cows[J]. Journal of dairy science, 2010, 93(9): 4045-4053.
|
2 |
张楷, 韩书庆, 程国栋, 等. 基于高斯混合-隐马尔科夫融合算法识别奶牛步态时相[J]. 智慧农业(中英文), 2022(2): 53-63.
|
|
ZHANG K, HAN S Q, CHENG G D, et al. Gait phase recognition of dairy cows based on Gaussian Mixture model and Hidden Markov model[J]. Smart agriculture, 2022(2): 53-63.
|
3 |
DE MOL R M, ANDRÉ G, BLEUMER E J, et al. Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows[J]. Journal of dairy science, 2013, 96(6): 3703-3712.
|
4 |
李小杉, 杨丰利. 奶牛肢蹄病对繁殖性能的影响[J]. 中国畜牧兽医, 2014, 41(5): 248-251.
|
|
LI X S, YANG F L. Effect of lameness on reproductive performance in dairy cows[J]. China animal husbandry & veterinary medicine, 2014, 41(5): 248-251.
|
5 |
CHA E, HERTL J A, BAR D, et al. The cost of different types of lameness in dairy cows calculated by dynamic programming[J]. Preventive veterinary medicine, 2010, 97(1): 1-8.
|
6 |
LEACH K A, TISDALL D A, BELL N J, et al. The effects of early treatment for hindlimb lameness in dairy cows on four commercial UK farms[J]. The veterinary journal, 2012, 193(3): 626-632.
|
7 |
PEZZUOLO A, GUARINO M, SARTORI L, et al. A feasibility study on the use of a structured light depth-camera for three-dimensional body measurements of dairy cows in free-stall barns[J]. Sensors (basel), 2018, 18(2): ID E673.
|
8 |
ZHENG Z Y, ZHANG X Q, QIN L F, et al. Cows' legs tracking and lameness detection in dairy cattle using video analysis and Siamese neural networks[J]. Computers and electronics in agriculture, 2023, 205: ID 107618.
|
9 |
LI Q, CHU M Y, KANG X, et al. Temporal aggregation network using micromotion features for early lameness recognition in dairy cows[J]. Computers and electronics in agriculture, 2023, 204: ID 107562.
|
10 |
POURSABERI A, BAHR C, PLUK A, et al. Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques[J]. Computers and electronics in agriculture, 2010, 74(1): 110-119.
|
11 |
WU D H, WU Q, YIN X Q, et al. Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector[J]. Biosystems engineering, 2020, 189: 150-163.
|
12 |
JIANG B, SONG H B, WANG H, et al. Dairy cow lameness detection using a back curvature feature[J]. Computers and electronics in agriculture, 2022, 194: ID 106729.
|
13 |
KANG X, LI S D, LI Q, et al. Dimension-reduced spatiotemporal network for lameness detection in dairy cows[J]. Computers and electronics in agriculture, 2022, 197: ID 106922.
|
14 |
LI Z Y, ZHANG Q R, LYU S C, et al. Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows[J]. Biosystems engineering, 2022, 218: 62-77.
|
15 |
ABDUL JABBAR K, HANSEN M F, SMITH M L, et al. Early and non-intrusive lameness detection in dairy cows using 3-Dimensional video[J]. Biosystems engineering, 2017, 153: 63-69.
|
16 |
ARAZO E, ALY R, MCGUINNESS K. Segmentation enhanced lameness detection in dairy cows from RGB and depth video[EB/OL]. arXiv: 2206.04449, 2022.
|
17 |
WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]// 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2023: 7464-7475.
|
18 |
KAY W, CARREIRA J, SIMONYAN K, et al. The kinetics human action video dataset[EB/OL]. arXiv: 1705.0695 0, 2017.
|
19 |
FARNEBÄCK G. Two-frame motion estimation based on polynomial expansion[C]// Image Analysis: 13th Scandinavian Conference, SCIA 2003. Berlin, Germany: Springer. 2003: 363-370.
|
20 |
HORN B K P, SCHUNCK B G. Determining optical flow[J]. Artificial intelligence, 1981, 17(1/2/3): 185-203.
|
21 |
LUCAS B D, KANADE T. An iterative image registration technique with an application to stereo vision[C]// IJCAI'81: 7th International Joint Conference on Artificial Intelligence. Vancouver, Canada: ACM, 1981: 674-679.
|
22 |
TU Z G, XIE W, ZHANG D J, et al. A survey of variational and CNN-based optical flow techniques[J]. Signal processing: Image communication, 2019, 72: 9-24.
|
23 |
ILG E, MAYER N, SAIKIA T, et al. FlowNet 2.0: Evolution of optical flow estimation with deep networks[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2017: 2462-2470.
|
24 |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]// Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.
|
25 |
LIN J, GAN C, HAN S. TSM: temporal shift module for efficient video understanding[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2019: 7083-7093.
|