基于特征自适应选择的金字塔均值漂移跟踪方法

针对均值漂移跟踪算法框架不足以对目标帧间运动过大及快速尺度变化进行有效地处理,且单个图像特征对环境适应性较差.提出了一种特征自适应选择方法,通过分析目标与背景的特征区分度来选择出最有效的特征.将金字塔自适应分解和均值漂移跟踪结合,提出了金字塔均值漂移跟踪方法.采用背景加权直方图描述目标模板模型,核函数加权直方图描述候选...

Pyramid Mean Shift Tracking Algorithm Based on Adaptive Feature Selection

Mean shift algorithm is a robust and rapid pattern matching algorithm. However, the algorithm has shortages to deal with the cases that the displacements of target between two successive frames are relatively large and the scales of target change quickly. Also, the adaptability of single feature is poor to the changeable circumstance. Therefore, the paper presents an adaptive feature selection method to determine the most effective feature by analyzing the discriminative value of target and background. By representing the target model and the target candidate in terms of background weighted histogram and kernel weighted histogram respectively, and using the pyramid analysis technique, the pyramid mean shift tracking method is proposed to localize target via a coarse-to-fine way. Furthermore, a scale update mechanism is presented. Experimental results on various videos show that the proposed method can successfully cope with the cases such as high-speed moving target, scale variations, camera motion, partial occlusions, etc.