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Farm animal behavior analysis is a crucial tasks for the industrial farming.In an indoor farm setting, extracting Key joints of animal is essential fortracking the animal for longer period of time. In this paper, we proposed adeep network named DUROCMIEN that exploit transfer learning to trained thenetwork for the Duroc, a domestic breed of pig, an end to end fashion. Thebackbone of the architecture is based on hourglass stacked dense-net. In orderto train the network, key frames are selected from the test data using K-meansampler. In total, 9 Keypoints are annotated that gives a brief detailedbehavior analysis in the farm setting. Extensive experiments are conducted andthe quantitative results show that the network has the potential of increasingthe tracking performance by a substantial margin.

Durocmien: A deep framework for duroc skeleton extraction in constraint environment