You are here

Improved Video Registration using Non-Distinctive Local Image Features

TitleImproved Video Registration using Non-Distinctive Local Image Features
Publication TypeConference Paper
Year of Publication2007
AuthorsHess, R., and A. Fern
Conference Name2007 IEEE Conference on Computer Vision and Pattern Recognition
Pagination1 - 8
Date Published06/2007
Conference LocationMinneapolis, MN
ISBN Number1-4244-1180-7
Keywordscomputer vision, feature extraction, image matching, image registration, video signal processing

The task of registering video frames with a static model is a common problem in many computer vision domains. The standard approach to registration involves finding point correspondences between the video and the model and using those correspondences to numerically determine registration transforms. Current methods locate video-to-model point correspondences by assembling a set of reference images to represent the model and then detecting and matching invariant local image features between the video frames and the set of reference images. These methods work well when all video frames can be guaranteed to contain a sufficient number of distinctive visual features. However, as we demonstrate, these methods are prone to severe misregistration errors in domains where many video frames lack distinctive image features. To overcome these errors, we introduce a concept of local distinctiveness which allows us to find model matches for nearly all video features, regardless of their distinctiveness on a global scale. We present results from the American football domain-where many video frames lack distinctive image features-which show a drastic improvement in registration accuracy over current methods. In addition, we introduce a simple, empirical stability test that allows our method to be fully automated. Finally, we present a registration dataset from the American football domain we hope can be used as a benchmarking tool for registration methods.