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GOAT FLOCK SURVEILLANCE: A VIDEO ANALYTICS FRAMEWORK USING QUADCOPTER

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dc.contributor.author K.A. Qazi
dc.contributor.author Z. Mehmood
dc.contributor.author T. Nawaz
dc.contributor.author H.A. Habib
dc.date.accessioned 2023-03-14T06:03:19Z
dc.date.available 2023-03-14T06:03:19Z
dc.date.issued 2018-03-09
dc.identifier.citation Qazi, K. A. (2018). Goat Flock Surveillance: A Video Analytics Framework Using Quadcopter. Pakistan Journal of Science, 70(1), 63-70. en_US
dc.identifier.issn 0300-9877
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/19063
dc.description.abstract In the present study, goat flock surveillance algorithm using video analytics was determined. The surveillance video camera was mounted over a quadcopter camera, which captured the videos of flocks. A video analytics algorithm using Haar features and the Ada Boost classifier was performed. The technique for tracking of flocks is based on the Kanade-Lucas-Tomasi feature (KLT) tracker. The algorithm presented here allows goat flock surveillance without human guidance and was helpful in real-time monitoring and management of goat flocks. The proposed algorithm made monitoring of goat flocks economical and could be applied to any kind of animal flock in different environments. The qualitative and quantitative analysis carried out for successful goat etection demonstrated the efficiency of the proposed algorithm as compared to the state-of-the-art goat flock rveillance and detection algorithms. The proposed algorithm successfully tracked the goat with accuracy of 93%. en_US
dc.language.iso en en_US
dc.publisher Lahore: Pakistan Association For The Advancement Of Science en_US
dc.subject Animal detection en_US
dc.subject Haar transform en_US
dc.subject Ada Boost en_US
dc.subject Kanade-Lucas-Tomasi en_US
dc.subject Quadcopter en_US
dc.title GOAT FLOCK SURVEILLANCE: A VIDEO ANALYTICS FRAMEWORK USING QUADCOPTER en_US
dc.type Article en_US


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