The classic mean-shift tracking algorithm has achieved success in the field of computer vision because of its speediness and efficiency. However, classic mean-shift tracking algorithm would fail to track in some complicated conditions such as some parts of the target are occluded, little color difference between the target and background exists, or sudden change of illumination and so on. In order to solve the problems, an improved algorithm is proposed based on the mean-shift tracking algorithm and adaptive fusion of features. Color, edges and corners of the target are used to describe the target in the feature space, and a method for measuring the discrimination of various features is presented to make feature selection adaptive. Then the improved mean-shift tracking algorithm is introduced based on the fusion of various features. For the purpose of solving the problem that mean-shift tracking algorithm with the single color feature is vulnerable to sudden change of illumination, we eliminate the effects by the fusion of affine illumination model and color feature space which ensures the correctness and stability of target tracking in that condition. Using a group of videos to test the proposed algorithm, the results show that the tracking correctness and stability of this algorithm are better than the mean-shift tracking algorithm with single feature space. Furthermore the proposed algorithm is more robust than the classic algorithm in the conditions of occlusion, target similar with background or illumination change.
In order to solve the problems that CV model can’t segment object which is partially occluded or has similar gray value with background or has obvious textures, we add shape restraint equations of prior shape to level set function, which keeps the curve to be a specific class shape in the whole evolvement, thus we realize shape preserving in object segmentation. In addition, we build an energy function for rectangle object using our proposed model, deduce a group of corresponding Euler-Lagrange ordinary differential functions and evolve the level set function. By evolution, rectangle object can be segmented, and the final level set function is just the quantitative description of the rectangle object. At last, we validate with three groups of experiments that our model can not only segment the rectangle object from complex backgrounds, but also has lessened calculation and strong robustness.
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