Dynamic Measurement Clustering to Aid Real Time Tracking

Abstract

We present a technique/or clustering measurements such that high-dimensional parameter estimation problems can be simplified. The key idea is to find rows of the measurement Jacobian whose rank is significantly less than its width. Such a set of rows gives a cluster of measurements which is affected only by a subset of the parameter space. This cluster can be used independently from other measurements to isolate parameter decisions. Unlike static partitioning techniques, the method presented dynamically generates clusters at each step of the estimation. This achieves substantial computational reductions, even for problems which cannot be partitioned in the traditional sense. The technique is applied to the task of tracking camera motions in real-time and video sequences are used to compare the resulting system to previous methods.