Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets [article]

Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
2016 arXiv   pre-print
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images. Discovered parts are used to generate more accurate appearance likelihoods for traditional part-based models like Pictorial Structures [13] and its derivatives. Our experiments on images of a hawkmoth in flight show that our proposed approach significantly improves over existing work [27] for this application, while also being more generally applicable. Our
more » ... sed approach localizes landmarks at least twice as accurately as a baseline based on a Mixture of Pictorial Structures (MPS) model. Our unique High-Resolution Moth Flight (HRMF) dataset is made publicly available with annotations.
arXiv:1605.00707v1 fatcat:muufpvzkfrcubgf2qs2t2uembm