Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scene Modelling, Recognition and Tracking with Invariant Image Features
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.