Estimation of Camera Parameters in Video Sequences with a Large Amount of Scene Motion
Jurandy Almeida, Rodrigo Minetto, Tiago A. Almeida, S. Torresand, Neucimar J. Leite · 2010
Most of existing techniques to estimate camera motion is based on analysis of the optical flow. However, such methods can be inaccurate and/or inefficiently when applied in video sequences which have a large amount of motion or a large number of scene changes. In this paper, we present an approach to estimate camera motion based on analysis of local invariant features. Such features are robust across a substantial range of affine distortion. Experiments on synthesized video clips with a fully controlled environment show that our technique is more effective than the optical flow-based approaches for estimating camera motion with a large amount of scene motion. To address this problem, we present an approach for the estimation of camera motion with a large amount of scene mo- tion. Our technique relies on analysis of local invariant features obtained from extrema in the scale space rather than on analysis of the optical flow. Such features are robust across a substantial range of affine distortion. In order to validate our approach, we use synthetic videos sequences based on POV-Ray scenes including all kinds of camera motion and many of their possible combinations. The main advantage of such a synthetic test set is that the camera motion parameters can be fully controlled. Further, we have conducted several experiments to show that our technique is more effective than the optical flow-based ones for estimating camera motion with a large amount of scene motion. The remainder of the paper is organized as follows. Sec- tion II presents our approach for the estimation of camera motion. The experimental settings and results are discussed in Section III. Finally, Section IV presents conclusions and direc- tions for future work. In presence of a substantial range of affine distortion, the methods for estimating camera motion by analysis of the optical flow can fail (3). To address this problem, we present an ap- proach for the estimation of camera motion based on the analysis of the local invariant features. It consists of three main steps: (1) feature matching; (2) motion model fitting; and (3) robust estimation of the camera parameters.