Fast Non-Rigid Object Boundary Tracking

This paper introduces a method which provides robust tracking results and accurately segmented object boundaries in short computation time. The first step of the algorithm is to apply a novel edge detector on efficiently calculated color probability maps in an object-specific Fisher color space. The proposed edge detector exploits context information by finding the maximally stable boundaries of connected regions in threshold results outperforming purely local edge detectors. Finally, based on the estimated edge maps a probabilistic particle filtering framework hypothesizes rigid transformations for initializing an active contour model to provide accurate object segmentations in each frame. Experimental evaluations show that robust tracking results with accurate segmentations are obtained on challenging data sets.