A complete system for head tracking using motion-based particle filter and randomly perturbed active contour
Nidhal Carla Bouaynaya, Dan Schonfeld · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Recent advances in multimedia and communication require techniques for accurately tracking objects in video sequences. We propose a complete system for head tracking and contour refinement. Our tracking approach is based on particle filtering framework. However, unlike existing methods that use prior knowledge or likelihood functions as proposal densities, we use a motion-based proposal. Adaptive Block Matching (ABM) algorithm is the motion estimation technique used. Several advantages arise from this choice of proposal. (i) Only few samples are propagated. (ii) The tracking is adaptive to different categories of motion (iii) Off-line motion learning is not needed. Following the tracking is the contour refinement step. We want to transform the parametric estimate representing the tracked head at a given time instant into an elastic contour delineating the head’s boundaries. We use an active contour framework based on a dynamic programming scheme. However active contours are very sensitive to parameter assignment and initial condition. Using the tracked parametric estimate, we create a set of randomly perturbed initial conditions. The optimal contour is then the one corresponding to the lowest energy. Our system demonstrates tracking a person’s head in complex environments and delineates its boundaries for future use.