We present a novel method for tracking multiple objects in video captured by a non-stationary camera. For low quality video,
estimation fails when the number of good matches shrinks below the minimum required to estimate the motion model. This paper extends
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
with the complex model only.