Active Segmentation and Adaptive Tracking Using Level Sets

We describe algorithms for active segmentation (AS) of the first frame, and subsequent, adaptive object tracking through succeeding frames, in a video sequence. Object boundaries that include different known colours are segmented against complex backgrounds; it is not necessary for the object to be homogeneous. As the object moves, we develop a tracking algorithm that adaptively changes the colour space model (CSM) according to measures of similarity between object and background. We employ a kernel weighted by the normalized Chamfer distance transform, that changes shape according to a level set definition, to correspond to changes in the perceived 2D contour as the object rotates or deforms. This improves target representation and localisation. Experiments conducted on various synthetic and real colour images illustrate the segmentation and tracking capability and versatility of the algorithm in comparison with results using previously published methods.