Bee pose estimation from single images with convolutional neural network
Bee pose estimation from single images with convolutional neural network
复制标题
使用卷积神经网络从单张图像估计蜜蜂姿势
DOI：
10.1109/icip.2017.8296800
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
O. Deussen
中科院分区：
文献类型：
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作者：
Le Duan;Minmin Shen;Wenjing Gao;Song Cui;O. Deussen
在本文中，我们提出了一个基于深度卷积神经网络（ConvNet）的框架，用于从单个图像中估计蜜蜂的姿态。与现有的一些人体姿态估计方法，本地化固定数量的身体关节，我们的方法处理的情况下，不同数量的目标。与现有的蜜蜂姿态估计方法相比，我们的框架是更强大和准确的。它甚至对一些具有挑战性的图像（例如，当蜜蜂用棍子喂糖水时）。建议的框架学习从蜜蜂的全局结构和局部外观到其姿势的映射。我们在两个具有挑战性的数据集上评估了我们的方法。实验结果表明，该算法较现有的昆虫姿态估计算法有明显的改进。
In this paper, we present a deep convolutional neural network (ConvNet) based framework for estimating the bee pose from a single image. Unlike some existing human pose estimation methods that localize a fixed number of body joints, our method handles the cases with a varying number of targets. Compared to the existing bee pose estimation methods, our framework is more robust and accurate. It is effective even for some challenging images (e.g., when the bee is fed sugar water with a stick). The proposed framework learns a mapping from the global structure and local appearance of a bee to its pose. We evaluated our method on two challenging datasets. Experiments showed that it has achieved significant improvements over the existing insect pose estimation algorithms.