Robust Registration of Long Sport Video Sequence

Abstract

Automatic registration plays an important role for a sport analysis system, the automation and accuracy of the registration for a long video sequence can still be an open problem for many practical applications. We propose a novel method to cope with it: (1) Reference frames can be introduced as a transaction of computing homography to map each frame of the imagery to the globally consistent model of the rink, that can reduce the accumulative error of successive registration and make the system more automatic. (2) An more distinctive invariant point feature (SIFT) can be used to provide reliable and robust matching across large range of affine distortion and change in illumination, that can improve the computational precision of homography. Experimental results show that the proposed algorithm is very efficient and effective on video recorded live by the authors in the World Short Track Speed Skating Championships.