Document Type



Computer Engineering | Robotics


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.

Article Number


Publication Date



20th International Conference on Advanced Information Networking and Applications (AINA 2006), 18-20 April 2006, Vienna, Austria

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

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Robotics Commons