目的 针对传统Mean Shift算法对受背景干扰的目标无法进行有效跟踪并缺少有效的模型更新策略的问题，提出一种将背景加权和选择性子模型更新相结合的跟踪算法。方法 首先，在Mean Shift框架下，为了减少背景信息对目标定位的干扰，利用目标区域周围像素的颜色直方图定义背景加权系数，并将该系数只引入到目标模型的颜色直方图中，从而建立一个新的目标模型。然后，根据目标模型中每个分量匹配贡献度的大小选取需要更新的模型分量及其更新公式。结果 实验结果表明，本文算法能够抑制背景干扰，同时能对模型进行有效的选择性更新，克服了整体更新策略严重的模型漂移问题。结论 本文从模型描述和更新策略两个方面对传统Mean Shift算法进行了改进，实验结果表明本文算法具有较好的有效性和鲁棒性。

Objective The traditional mean shift tracking algorithmoften loses the targetwhen the object is similar to the background and lacks an effective model update strategy. To address these challenges, this study proposes a new visual object tracking Method. Method First, coefficients based on the color histograms of the background pixels around theobject are computedand introduced to the target model to reduce the location error. Second, sub-modelsare selected and updatedaccording to the matchcontributing degree in the current frame. Result Results show that the proposed Method can well restrain background distractions, while effectively updating the target model and eliminating the driftingphenomenon. Conclusion This paper proposes avisual object tracking Method based on weighted background and selective sub-model update strategy to improve the traditional mean shift tracking algorithm in terms of object modeling and model update strategy.Results show that the proposed Method is effective and robust.