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Vol. 8, Issue 1, Part B (2023)

Analysis of a hybrid support vector machine-based laying-hen tracking technique

Author(s): Ajao Kehinde, Gbolagade Monsurat and Idris Muyideen
Abstract: Animal wellbeing may be evaluated by looking at their behaviour. The approach that is most often employed to research animal behaviour is manual examination of videos. This strategy, however, is time-consuming and is based on the analysts' opinions. Automated identification of individual animals is urgently required, and automatic tracking is a key component of the solution to this issue. For the automatic tracking of individual laying hens in a layer group, a Hybrid Support Vector Machine (HSVM)-based algorithm was created in this work. An experimental platform was used to film laying chickens grown beneath a floor system for more than 500 hours. Based on their overlap rates and average Overlap rates, the experimental results showed that the HSVM tracker outperformed the Frag (fragment-based tracking method), the TLD (Tracking-Learning-Detection), the PLS (object tracking via partial least squares analysis), the Mean Shift Algorithm, and the Particle Filter Algorithm. According to the experimental findings, the HSVM tracker outperformed the other examined algorithms in terms of resilience and state-of-the-art performance when it came to tracking individual laying hens. Under realistic rearing circumstances, it may be useful for observing animal behaviour.
Pages: 106-115  |  265 Views  5 Downloads


International Journal of Veterinary Sciences and Animal Husbandry
How to cite this article:
Ajao Kehinde, Gbolagade Monsurat, Idris Muyideen. Analysis of a hybrid support vector machine-based laying-hen tracking technique. Int J Vet Sci Anim Husbandry 2023;8(1):106-115. DOI: https://doi.org/10.22271/veterinary.2023.v8.i1b.477
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International Journal of Veterinary Sciences and Animal Husbandry