论文标题
杜洛基（Durocmien）：在约束环境中杜罗克骨架提取的深层框架
Durocmien: A deep framework for duroc skeleton extraction in constraint environment
论文作者
论文摘要
农场动物行为分析是工业农业的关键任务。在室内农场环境中，提取动物的关键关节对于在更长的时间内追踪动物至关重要。在本文中，我们提出了一个名为Durocmien的深网，该网络利用转移学习来培训了杜罗克（Duroc）的网络，杜罗克（Duroc）是一种猪的家用，是一种端到端时尚。该体系结构的骨干基于沙漏堆积的密集网络。为了训练网络，使用K-均值采样器从测试数据中选择关键帧。总共有9个关键点注释，在农场环境中提供了简短的详细行为分析。进行了广泛的实验，定量结果表明，该网络具有将跟踪性能提高的潜力。
Farm 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 named DUROCMIEN that exploit transfer learning to trained the network for the Duroc, a domestic breed of pig, 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.
