结合人眼非均匀采样特性和曲线演化的红外目标跟踪方法

Infrared Object Tracking Method Using Human Eye Non-uniform Sampling Characteristic and Curves Evolving

在成像制导过程中需要实时处理大量信息。为了尽可能在保留有效信息情况下降低计算量,提出了一种基于人眼非均匀采样特性和水平集曲线演化方法相结合的红外目标跟踪方法。首先利用对数极坐标模型的旋转、缩放及灰度分布不变性来压缩信息量,以提高计算速度。然后采用基于目标灰度和边缘特征的水平集曲线演化方法来抑制目标的非刚性形变,从而实现对非刚性变形目标的稳健跟踪。与传统的Mean Shift跟踪方法和粒子滤波跟踪方法相比,该方法具有跟踪稳定、精度高等优点。实验结果表明,该方法能够有效抑制目标的非刚性形变。

Large quantity information needs to be dealt during image guidance. In order to compress calculation quantity,a novel method for tracking infrared target based on curves evolving theory and human eye non-uniform sampling characteristic was proposed. First,log-polar coordinate transform model was used to compress calculation quantity and increase calculating speed. And then,levcl sets curves evolving method based on target intensity and edge features was presented to suppress local deformation, thus achieving non-rigid deformed target tracking. Compared with traditional Mean Shift tracking method, this method is stable and efficient. Experimental results show the proposed method is effective for suppress non-rigid deformed.