Robust centroid target tracker based on novel distance features in cluttered image sequences

A real-time adaptive segmentation method based on new distance features is proposed for the binary centroid tracker. These novel features are distances between the predicted center pixel of a target object by a tracking filter and each pixel in extraction of a moving target. The proposed method restricts clutters with target-like intensity from entering a tracking window and has low computational complexity for real-time applications compared with other complex feature-based methods. Comparative experiments show that the proposed method is superior to other segmentation methods based on the intensity feature only in target detection and tracking. key words: centroid tracker, distance feature, target segmentation, Bayes decision rule