Visual Tracking Based on Adaptive Mean Shift Multiple Appearance Models

Abstract

To overcome the tracking issues caused by the complex environment namely, illumination variation and background clutters, tracking algorithm was proposed based on multi-cues fusion to construct a robust appearance model, indeed the global motion is estimated using the H∞ filter based on the nearly constant velocity motion model, then the traditional Mean Shift (MS) estimate the local state associated with each sub appearance model, finally the weights of the sub appearance models are adjusted and combined to estimate the final state. The proposed method is tested on public videos that present different environment issues. Experiences and comparisons conducted show the robustness of our methods in challenging tracking conditions.