A method for tracking elliptical contour of arbitrary-pose head based on particle filter

Based on particle filters, a head elliptical contour tracking method applicable to complex background, large distance range and arbitrary pose is proposed in this paper. The method applies the strategy of dual stochastic sampling: the uniform sampling around predicted value is utilized to produce initial particles, which can ensure particles' diversity; after weights updated, the Gaussian sampling is adopted to resample the particles to achieve high convergence. In weights updating, color matching between template elliptical sub-image and particle elliptical sub-image is implemented by a kind of block histogram, and the distance to maximum gradient point (DMG) on normal line is applied to measure out the particle ellipses' similarity to head edges, and finally, by the D-S theory the above two measurements are fused to update the particles' weights. The experiments results confirm the method's validity, robustness to complex background and arbitrary head pose, and applicability to large distance range.