Intelligent video tracking based on fuzzy-reasoning segmentation

In our previous work [J. Cho, D. Kim, D. Park, Robust centroid target tracker based on new distance features in cluttered image sequences. IEICE Transactions on Information and Systems, Vol. E83-D, No. 12, December, 2000.], we presented a novel centroid target tracker based on new distance features in cluttered image sequences. A real-time adaptive segmentation method based on new distance features was proposed for the binary centroid tracker. The target classifier by the Bayes decision rule for minimizing the probability of error should properly estimate the state-conditional densities. In this correspondence, the proposed target classifier adopts the fuzzy-reasoning segmentation instead of the estimation of the state-conditional probability densities. Comparative experiments show that the performance of the proposed fuzzy-reasoning segmentation is superior to that of the conventional thresholding methods. The usefulness of the fuzzy-reasoning segmentation for practical applications is demonstrated by considering two sequences of real target images. The tracking results are good and stable without difficulty of the probability densities estimation.