Robust methods based on nonlinear influence functions are often used to remove outliers from data. The article describes the design of an algorithm for tracking a moving object in a thermal image using a SURF descriptor and robust Kalman filter. However, given that the main shortfall of robust methods is reduced efficiency, tunable parameters of the robust influence function are used to achieve a balance between robustness and efficiency. The parameters adapt to the observed scene's current conditions, while their estimation is based on the calculated data outlier contamination factor. The outliers that contaminate the data are generally a result of various types of occlusions. The results show that the proposed solution achieves better performance than the standard Kalman filter or fixed‐parameter robust Kalman filter.

中文翻译：

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使用热像仪对运动对象进行稳健跟踪，并加快稳健特征描述符的速度

基于非线性影响函数的鲁棒方法通常用于从数据中去除异常值。本文介绍了一种使用SURF描述符和鲁棒卡尔曼滤波器跟踪热图像中运动对象的算法的设计。但是，鉴于鲁棒方法的主要缺点是效率降低，因此可以使用鲁棒影响函数的可调参数来实现鲁棒性和效率之间的平衡。这些参数适用于观察到的场景的当前条件，而其估计则基于计算得出的数据离群值污染因子。污染数据的异常值通常是各种类型的遮挡的结果。结果表明，与标准卡尔曼滤波器或固定参数鲁棒卡尔曼滤波器相比，所提出的解决方案具有更好的性能。