Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Insect pose estimation, ConvNet |
Field | DocType | ISSN |
Computer vision,Global structure,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Pose,Artificial intelligence,SUGAR/WATER,Body joints | Conference | 1522-4880 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Duan | 1 | 1 | 1.04 |
| Minmin Shen | 2 | 1 | 1.04 |
| Wenjing Gao | 3 | 1 | 0.36 |
| Song Cui | 4 | 1 | 0.36 |
| Oliver Deussen | 5 | 2852 | 205.16 |