Combinatorial Fusion Criteria for Real-Time Tracking

We address the problem of automated video tracking of targets when targets undergo multiple mutual occlusions. Our approach is based on the idea that as targets are occluded, selection of feature subsets and combinations of those features are effective in identifying the target and improving tracking performance. We use combinatorial fusion analysis to develop a metric to select which subset of features 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 to illustrate the performance of the proposed metric