English text is difficult to recognize under the interference of blurred background, so it is necessary to improve the fixed-point tracking technology of English text. Based on machine learning algorithms, this paper studies the fixed-point tracking model of English reading text based on mean shiftand multi-feature fusion. The target tracking algorithm based on mean shift obtains the description of the target model and the candidate model by calculating the pixel feature probability in the target area and the candidate area. Then, it uses the similarity function to measure the similarity between the initial frame target model and the current candidate model, selects the candidate model that maximizes the similarity function, and obtains the target model mean offset vector. Finally, it continuously iteratively calculates the offset vector based on this vector, and finally converges to the true position of the target, thereby achieving the effect of tracking. In general, it is verified that the model constructed in this paper works well through control experiments.

中文翻译：

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基于均值漂移和多特征融合的英语阅读文本定点跟踪

英文文本在模糊背景的干扰下难以识别，因此有必要改进英文文本的定点跟踪技术。基于机器学习算法，研究了基于均值漂移和多特征融合的英语阅读文本定点跟踪模型。基于均值漂移的目标跟踪算法通过计算目标区域和候选区域中的像素特征概率来获得目标模型和候选模型的描述。然后，它使用相似度函数测量初始帧目标模型与当前候选模型之间的相似度，选择使相似度函数最大化的候选模型，并获得目标模型平均偏移矢量。最后，它根据该向量不断地迭代计算偏移向量，最后收敛到目标的真实位置，从而达到跟踪的效果。总体而言，通过控制实验可以验证本文构建的模型工作良好。