Behaviour recognition of housed sheep based on spatio-temporal information

References

• Barbara RR, Nguyen T, Sujani S, White RR. 2023. Open-source wearable sensors for behavioral analysis of sheep undergoing heat stress. Journal title. 13:9281.
   Google Scholar
• Bhujel A, Arulmozhi E, Moon B, Kim, HT. 2021. Deep-learning-based automatic monitoring of pigs’ physico-temporal activities at different greenhouse gas concentrations. Animals (Basel). 11(11):3089.
   PubMedGoogle Scholar
• Birthe K, Jürgen H, Alexander R, Marcus C, Martin H. 2024. Comparative study of feeding and rumination behaviour of goats and sheep fed mixed grass hay of different chop length. Journal of Animal Physiology and Animal Nutrition. 1–11.
   PubMed Web of Science ®Google Scholar
• Cheng M, Yuan H, Wang Q, Cai Z, Liu Y, Zhang Y. 2022. Application of deep learning in sheep behaviors recognition and influence analysis of training data characteristics on the recognition effect. Comput Electron Agric. 198:107010. doi:10.1016/j.compag.2022.107010.
   Web of Science ®Google Scholar
• Decandia M, Giovanetti V, Molle G, Acciaro M, Mameli M, Cabiddu A, Cossu R, Serra MG, Manca C, Rassu SPG, et al. 2018. The effect of different time epoch settings on the classification of sheep behaviour using tri-axial accelerometry. Comput Electron Agric. 154:112–119. doi:10.1016/j.compag.2018.09.002.
   Web of Science ®Google Scholar
• Fang H, Xie S, Tai Y, Cewu L. 2016. Rmpe: regional multi-person pose estimation. IEEE. doi:10.48550/arXiv:1612.00137v1.
   Google Scholar
• Giovanetti V, Decandia M, Molle G, Acciaro M, Mameli M, Cabiddu A, Cossu R, Serra MG, Manca C, Rassu SPG, et al. 2017. Automatic classification system for grazing, ruminating and resting behaviour of dairy sheep using a tri-axial accelerometer. Livest Sci. 196:42–48. doi:10.1016/j.livsci.2016.12.011.
   Web of Science ®Google Scholar
• Hu Y, Zhan J, Zhou G, Chen A, Cai W, Guo K, Hu Y, Li L. 2022. Fast forest fire smoke detection using MVMNet. Knowl Based Syst. 241:108219. doi:10.1016/j.knosys.2022.108219.
   Web of Science ®Google Scholar
• Kashiha MA, Bahr C, Ott S, Moons CPH, Niewold TA, Tuyttens F, Berckmans D. 2014. Automatic monitoring of pig locomotion using image analysis. Livest Sci. 159:141–148. doi:10.1016/j.livsci.2013.11.007.
   Web of Science ®Google Scholar
• Man C, Hongbo Y, Qifan W, Zhenjiang C, Yueqin L, Yingjie Z. 2022. Application of deep learning in sheep behaviours recognition and influence analysis of training data characteristics on the recognition effect. Computers and Electronics in Agriculture. 198:107010.
   Web of Science ®Google Scholar
• Mansbridge N, Mitsch J, Bollard N, Ellis K, Miguel-Pacheco G, Dottorini T, Kaler J. 2018. Feature selection and comparison of machine learning algorithms in classification of grazing and rumination behaviour in sheep. Sensors. 18:3532. doi:10.3390/s18103532.
   PubMed Web of Science ®Google Scholar
• Meunier B, Pradel P, Sloth KH, Cirié C, Delval E, Mialon MM, Veissier I. 2018. Image analysis to refine measurements of dairy cow behaviour from a real-time location system. Biosystems Eng. 173:32–44. doi:10.1016/j.biosystemseng.2017.08.019.
   Web of Science ®Google Scholar
• Nasirahmadi A, Sturm B, Edwards S, Jeppsson K-H, Olsson A-C, Müller S, Hensel O. 2019. Deep learning and machine vision approaches for posture detection of individual pigs. Sensors. 19:3738. doi:10.3390/s19173738.
   PubMedGoogle Scholar
• Wei JC, Tang X, Liu JX, Zhang Z. 2023. Detection of pig movement and aggression using deep learning approaches. Animals (Basel). 13(19):3074.
   PubMedGoogle Scholar
• Wojke N, Bewley A, Paulus D. 2017. Simple online and real-time tracking with a deep association metric. Proceedings of the IEEE International Conference on Image Processing (ICIP). p. 3645–3649.
   Google Scholar
• Yang Q, Xiao D, Cai J. 2021. Pig mounting behaviour recognition based on video spatial–temporal features. Biosystems Eng. 206:55–66. doi:10.1016/j.biosystemseng.2021.03.011.
   Web of Science ®Google Scholar
• Ying Y., Zeng S., Zhao A., Yan. 2020. Recognition method for prenatal behavior of ewes based on the acquisition nodes of the collar. Transactions of the Chinese Society of Agricultural Engineering. 36(21):210–219.
   Google Scholar
• Zhang K, Li D, Huang J, Chen Y. 2020. Automated video behavior recognition of pigs using two-stream convolutional networks. Sensors. 20(4):1085. doi:10.3390/s20041085.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Wen GZ, Mi S, Zhang M, Geng X. 2022. Overview on 2D human pose estimation based on deep learning. J Softw. 33(11):4173–4191.
   Google Scholar
• Zheng C, Zhu X, Yang X, Wang L, Tu S, Xue Y. 2018. Automatic recognition of lactating sow postures from depth images by deep learning detector. Comput Electron Agric. 147:51–63. doi:10.1016/j.compag.2018.01.023.
   Web of Science ®Google Scholar
• Zhu W, Guo Y, Jiao P, Ma C-h, Chen C. 2017. Recognition and drinking behaviour analysis of individual pigs based on machine vision. Livest Sci. 205:129–136. doi:10.1016/j.livsci.2017.09.003.
   Web of Science ®Google Scholar