Selecting and Evaluating Combinatorial Fusion Criteria to Improve Multitarget Tracking

In many useful video tracking situations, targets move through repeated mutual occlusions. As targets undergo occlusions, the feature subsets and combinations of those features that are effective in identifying the target and improving tracking performance may change. We use combinatorial fusion analysis to select and evaluate criteria by which to identify the combination of features that will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results from three diverse video sequences to illustrate the performance of the proposed criteria