Joint Random Sample Consensus and Multiple Motion Models for Robust Video Tracking
We present a novel method for tracking multiple objects in video captured by a non-stationary camera. For low quality video, ransac estimation fails when the number of good matches shrinks below the minimum required to estimate the motion model. This paper extends ransac in the following ways: (a) Allowing multiple models of different complexity to be chosen at random; (b) Introducing a conditional probability to measure the suitability of each transformation candidate, given the object locations in previous frames; (c) Determining the best suitable transformation by the number of consensus points, the probability and the model complexity. Our experimental results have shown that the proposed estimation method better handles video of low quality and that it is able to track deformable objects with pose changes, occlusions, motion blur and overlap. We also show that using multiple models of increasing complexity is more effective than just using ransac with the complex model only.
KeywordsFeature Point Motion Estimation Transformation Model Motion Model Previous Frame
- 5.Grabner, M., Grabner, H., Bischof, H.: Learning features for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, June 2007, pp. 1–8 (2007)Google Scholar
- 8.Malik, S., Roth, G., McDonald, C.: Robust corner tracking for real-time augmented reality. In: VI 2002, p. 399 (2002)Google Scholar
- 10.Simon, G., Fitzgibbon, A.W., Zisserman, A.: Markerless tracking using planar structures in the scene. In: IEEE and ACM International Symposium on Augmented Reality (ISAR 2000). Proceedings (2000)Google Scholar
- 11.Skrypnyk, I., Lowe, D.G.: Scene modelling, recognition and tracking with invariant image features. In: ISMAR 2004, Washington, DC, USA, pp. 110–119. IEEE Comp. Society, Los Alamitos (2004)Google Scholar
- 12.Li, X.-R., Li, X.-M., Li, H.-L., Cao, M.-Y.: Rejecting outliers based on correspondence manifold. Acta Automatica Sinica (2008)Google Scholar