In view of the problem of satellite video point-target tracking , a method of Bayesian cl assification for tracking with the constraint of motion smoothness is proposed ,which named Bayesi an MoST .The idea of naive Bayesi an cl assification without relying on any prior probability of target is introduced .Under the constraint of motion smoothness ,the gray level similarity feature is used to describe the likelihood of the target .And then ,the simplified conditional probability correction model of classifier is created according to the independence assumption Bayes theorem .Afterwards ,the tracking target position can be determined by estimating the target posterior probability on the basis of the model .Meanwhile ,the Kalman filter , an assistance and optimization method ,is used to enhance the robustness of tracking processing .The theoretical method proposed are validated in a number of six experiments using SkySat and JL1H video ,each has two segments .The experiment results show that the BMoST method proposed have good performance ,the tracking precision is about 90% and tracking trajectory is smoothing .The method could satisfy the needs of the following advanced treatment in satellite video .%针对卫星视频条件下的点目标跟踪问题,提出了一种运动平滑约束的贝叶斯分类目标跟踪方法(BMoST).本方法引入朴素贝叶斯分类器的思想,不依赖目标的任何先验概率,在运动平滑性约束下,利用灰度相似性特征来表达描述目标的似然度,并根据独立假设的贝叶斯定理,建立简化的分类器条件概率修正模型,通过该模型估计目标的后验概率,从而实现目标跟踪.同时,采用卡尔曼滤波辅助、优化跟踪处理,提高算法的稳健性.试验数据采用SkySat和吉林一号拍摄的视频各两段,对6个点目标进行跟踪试验.结果表明,本文提出的方法针对卫星视频的点目标跟踪效果良好,精度达到90%左右,且跟踪轨迹平滑,满足卫星视频后续高级处理和应用需要.
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