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Automatic movement pattern analysis for data-driven system optimisation – an example for fattening livestock farming monitoring system
Published online by Cambridge University Press: 16 May 2024
Abstract
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This paper introduces a method for analysing motion patterns that can be utilised to optimise data-driven systems. The aim is to use surveillance cameras and artificial intelligence to track multiple objects in a reliable manner, thereby preserving the authenticity of movement patterns for numerous and similar objects. In a case study, this method is applied to optimize lighting conditions in animal husbandry. Furthermore, this approach can be utilized not only in animal husbandry but also in other domains.
- Type
- Artificial Intelligence and Data-Driven Design
- Information
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- The Author(s), 2024.
References
Bartels, T., Stuhrmann, R.A., Krause, E.T., Schrader, L., 2020. Research Note: Injurious pecking in fattening turkeys (Meleagris gallopavo f. dom.)—video analyses of triggering factors and behavioral sequences in small flocks of male turkeys. Poultry Science 99, 6326–6331. https://doi.org/10.1016/j.psj.2020.09.016CrossRefGoogle ScholarPubMed
Bretzner, L., Lindeberg, T., 1998. Feature Tracking with Automatic Selection of Spatial Scales. Computer Vision and Image Understanding 71, 385–392. https://doi.org/10.1006/cviu.1998.0650CrossRefGoogle Scholar
Campesato, O., 2020. Artificial Intelligence, Machine Learning, and Deep Learning. Mercury Learning and Information.CrossRefGoogle Scholar
DeCarlo, D., Metaxas, D., 2000. Optical Flow Constraints on Deformable Models with Applications to Face Tracking. International Journal of Computer Vision 38, 99–127. https://doi.org/10.1023/A:1008122917811CrossRefGoogle Scholar
Dick, T., Perez, C., Jagersand, M., Shademan, A., 2013. Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search. https://doi.org/10.15607/RSS.2013.IX.044CrossRefGoogle Scholar
Dimakopoulos, V.V., 2014. Parallel Programming Models, in: Torquati, M., Bertels, K., Karlsson, S., Pacull, F. (Eds.), Smart Multicore Embedded Systems. Springer, New York, NY, pp. 3–20. https://doi.org/10.1007/978-1-4614-8800-2_1CrossRefGoogle Scholar
Ma, Don Yitong, 2022. Summary of Research on Application of Deep Learning in Image Recognition. Highlights in Science, Engineering and Technology 1, 72–77. https://doi.org/10.54097/hset.v1i.429Google Scholar
Giorgi, R., Scionti, A., 2015. A scalable thread scheduling co-processor based on data-flow principles. Future Generation Computer Systems 53, 100–108. https://doi.org/10.1016/j.future.2014.12.014CrossRefGoogle Scholar
Guzhva, O., Ardö, H., Nilsson, M., Herlin, A., Tufvesson, L., 2018. Now You See Me: Convolutional Neural Network Based Tracker for Dairy Cows. Frontiers in Robotics and AI 5.CrossRefGoogle Scholar
Hagen, S., Brinker, J., Gembarski, P.C., Lachmayer, R., Thomas, O., 2021. Integration von Smarten Produkten und Dienstleistungen im IoT-Zeitalter – Ein Graph-basierter Entwicklungsansatz, in: Meinhardt, S., Wortmann, F. (Eds.), IoT – Best Practices: Internet der Dinge, Geschäftsmodellinnovationen, IoT-Plattformen, IoT in Fertigung und Logistik, Edition HMD. Springer Fachmedien, Wiesbaden, pp. 245–258. https://doi.org/10.1007/978-3-658-32439-1_14CrossRefGoogle Scholar
Kieseberg, P., Schrittwieser, S., Frühwirt, P., Weippl, E., 2019. Analysis of the Internals of MySQL/InnoDB B+ Tree Index Navigation from a Forensic Perspective, in: 2019 International Conference on Software Security and Assurance (ICSSA). Presented at the 2019 International Conference on Software Security and Assurance (ICSSA), pp. 46–51. https://doi.org/10.1109/ICSSA48308.2019.00013CrossRefGoogle Scholar
Lachmayer, R., Mozgova, I., 2022. Technical Inheritance as an Approach to Data-Driven Product Development, in: Krause, D., Heyden, E. (Eds.), Design Methodology for Future Products: Data Driven, Agile and Flexible. Springer International Publishing, Cham, pp. 47–64. https://doi.org/10.1007/978-3-030-78368-6_3CrossRefGoogle Scholar
Lachmayer, R., Mozgova, I., Reimche, W., Colditz, F., Mroz, G., Gottwald, P., 2014. Technical Inheritance: A Concept to Adapt the Evolution of Nature to Product Engineering. Procedia Technology, 2nd International Conference on System-Integrated Intelligence: Challenges for Product and Production Engineering 15, 178–187. https://doi.org/10.1016/j.protcy.2014.09.070CrossRefGoogle Scholar
Li, N., Ren, Z., Li, D., Zeng, L., 2020. Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. animal 14, 617–625. https://doi.org/10.1017/S1751731119002155CrossRefGoogle ScholarPubMed
Navarro, C.A., Carrasco, R., Barrientos, R.J., Riquelme, J.A., Vega, R., 2021. GPU Tensor Cores for Fast Arithmetic Reductions. IEEE Transactions on Parallel and Distributed Systems 32, 72–84. https://doi.org/10.1109/TPDS.2020.3011893CrossRefGoogle Scholar
Neethirajan, S., 2022. ChickTrack – A quantitative tracking tool for measuring chicken activity. Measurement 191, 110819. https://doi.org/10.1016/j.measurement.2022.110819CrossRefGoogle Scholar
Ohashi, T., Saijo, M., Suzuki, K., Arafuka, S., 2023. Deciphering the Drivers of Smart Livestock Technology Adoption in Japan: A Scoping Review, Expert Interviews, and Grounded Theory Approach. https://doi.org/10.48550/arXiv.2307.03338CrossRefGoogle Scholar
Raveendran, G., Homeyer, K., Raabe, J., Bartels, T., Lachmayer, R., 2023. Beleuchtung als Einflussfaktor für eine tiergerechte Mastputenhaltung. Juni.Google Scholar
Schmidt, T.B., Lancaster, J.M., Psota, E., Mote, B.E., Hulbert, L.E., Holliday, A., Woiwode, R., Pérez, L.C., 2022. Evaluation of a novel computer vision-based livestock monitoring system to identify and track specific behaviors of individual nursery pigs within a group-housed environment. Translational Animal Science 6, txac082. https://doi.org/10.1093/tas/txac082CrossRefGoogle ScholarPubMed
Shin, Y., Choi, K., 1996. Thread-based software synthesis for embedded system design, in: Proceedings ED&TC European Design and Test Conference. Presented at the Proceedings ED&TC European Design and Test Conference, pp. 282–286. https://doi.org/10.1109/EDTC.1996.494314CrossRefGoogle Scholar
Üreten, S., Eisenmann [2, M., Nelius [2, T., Cao [3, S., Matthiesen [2, S., Krause [1, D., 2019. A Concept Map for Design Method Experiments in Product Development – A Guideline for Method Developers. DS 98: Proceedings of the 30th Symposium Design for X (DFX 2019) 147–158. https://doi.org/10.35199/dfx2019.13CrossRefGoogle Scholar
van Vugt, S., 2008. Installing Ubuntu Server, in: van Vugt, S. (Ed.), Beginning Ubuntu LTS Server Administration: From Novice to Professional. Apress, Berkeley, CA, pp. 1–27. https://doi.org/10.1007/978-1-4302-1081-8_1Google Scholar
Zhan, B., Monekosso, D., Remagnino, P., Velastin, S., Xu, L.-Q., 2008. Crowd analysis: A survey. Mach. Vis. Appl. 19, 345–357. https://doi.org/10.1007/s00138-008-0132-4CrossRefGoogle Scholar
Zhang, Z., Liu, H., Meng, Z., Chen, J., 2019. Deep learning-based automatic recognition network of agricultural machinery images. Computers and Electronics in Agriculture 166, 104978. https://doi.org/10.1016/j.compag.2019.104978CrossRefGoogle Scholar
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