This paper presents a new technique for video segmentation and tracking. As most of segmentation techniques it consists on anvinitial model generation process with a subsequent object tracking phase. The model generation is accomplished by means of a combination of temporal and spatial transitions detection approaches. The novelty of the method is that these approaches are performed block-by-block. This has the advantages to reduce problems relevant to the object connectivity and to drastically decrease the algorithm computational complexity respect to a pixel-by-pixel processing procedure. According to the proposed strategy, edged blocks are firstly extracted in an active region selected by the user. From these, the subset of blocks that represents the object contour is selected by minimizing a cost function that exploits a multiple edged block feature vector: motion, smoothness, continuity, strongness and position. The tracking task is then performed by estimating the model blocks motion.

Experiments are presented that show comparable results accuracy respect to existing segmentation techniques while requiring a reduced computational complexity.