Particle Filter Algorithm for Object Tracking Based on Color Local Entropy

To achieve accurate visual object tracking and overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a particle filter for object tracking algorithm based on color local entropy (CLE) is proposed. First we improved the traditional histogram weighted function by using a scale factor. Then, for the shortcoming that the color feature is sensitive to illumination and environmental interference, a color local entropy object observation model is constructed by mapping the object from color feature space to local entropy space. In addition, an adaptive updating strategy of the object template is designed and the number of particle is adjusted dynamically according to the tracking performance. The experimental results show that compared with several existing algorithms, the proposed algorithm is more effective and robust for the real-time object tracking under the situation of illumination variation, object occlusion, and nonlinear motion