Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006 at the SPIE Defense and Security Symposium Symposium,17-21 April 2006, Orlando (Kissimmee), FL.

This research was conducted at the Fordham University Robotics and Computer Vision Lab. For more information about graduate programs in Computer Science, see, and the Fordham University Graduate School of Arts and Sciences, see


Computer Engineering | Robotics


We address the problem of selecting features to improve automated video tracking of targets that undergo multiple mutual occlusions. As targets are occluded, different 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 dynamically 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.

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