Version 1
: Received: 17 January 2020 / Approved: 19 January 2020 / Online: 19 January 2020 (04:40:15 CET)
How to cite:
Khan, A.Q.; Khan, S. A Bottom-up Approach for Pig Skeleton Extraction Using RGB Data. Preprints2020, 2020010208. https://doi.org/10.20944/preprints202001.0208.v1
Khan, A.Q.; Khan, S. A Bottom-up Approach for Pig Skeleton Extraction Using RGB Data. Preprints 2020, 2020010208. https://doi.org/10.20944/preprints202001.0208.v1
Khan, A.Q.; Khan, S. A Bottom-up Approach for Pig Skeleton Extraction Using RGB Data. Preprints2020, 2020010208. https://doi.org/10.20944/preprints202001.0208.v1
APA Style
Khan, A.Q., & Khan, S. (2020). A Bottom-up Approach for Pig Skeleton Extraction Using RGB Data. Preprints. https://doi.org/10.20944/preprints202001.0208.v1
Chicago/Turabian Style
Khan, A.Q. and Salman Khan. 2020 "A Bottom-up Approach for Pig Skeleton Extraction Using RGB Data" Preprints. https://doi.org/10.20944/preprints202001.0208.v1
Abstract
Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
