Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1925
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dc.contributor.authorBoteju, W. J. M-
dc.contributor.authorHerath, H. M. K. S-
dc.contributor.authorPeiris, M. D. P-
dc.contributor.authorWathsala, A. K. P. E-
dc.contributor.authorSamarasinghe, P-
dc.contributor.authorWeerasinghe, L-
dc.date.accessioned2022-04-06T09:25:01Z-
dc.date.available2022-04-06T09:25:01Z-
dc.date.issued2020-12-03-
dc.identifier.citationW. J. M. Boteju, H. M. K. S. Herath, M. D. P. Peiris, A. K. P. E. Wathsala, P. Samarasinghe and L. Weerasinghe, "Deep Learning Based Dog Behavioural Monitoring System," 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020, pp. 82-87, doi: 10.1109/ICISS49785.2020.9315983.en_US
dc.identifier.isbn978-1-7281-7089-3-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1925-
dc.description.abstractDogs are one of the most popular pets in the world. It is usual that pet owners are always concerned about the health and the wellbeing of their pets. The activity levels of the dogs vary from each other based on breed and age. Tracking the behavioral changes using image processing and machine learning concepts and notifying the pet owners via a mobile application is the main objective of this research. Breed recognition has been done applying deep learning concepts to the user-uploaded video or the photograph of the dog. This research mainly focuses on walking, running, resting, and barking activity patterns of the dog. A surveillance camera and sensors were the main equipment for data collection. The audio feature of the surveillance camera is used to identity the barking behavior of the dog. Dogs from different ages belonging to Pomeranian and German Shepherd breeds have been selected for this experiment. Transfer learning with ResNet50, Inception V3, and support vector machines have been used to recognize and classify the activities of the dogs. The research study was able to achieve the accuracy levels as follows: - breed recognition - 89%+, walking pattern recognition - 99.5%, resting pattern recognition - 97% and barking pattern recognition - 60%. With the above accuracy levels, the research was able to identify the unusual behaviour of the dogs.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 3rd International Conference on Intelligent Sustainable Systems (ICISS);Pages 82-87-
dc.subjectDeep Learningen_US
dc.subjectDog Behaviouralen_US
dc.subjectMonitoring Systemen_US
dc.subjectLearning Baseden_US
dc.titleDeep Learning Based Dog Behavioural Monitoring Systemen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICISS49785.2020.9315983en_US
Appears in Collections:Department of Computer Science and Software Engineering-Scopes
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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