一种基于自适应特征选择的目标实时跟踪算法

A Real-time Object Tracking Algorithm Based on Self-adaptive Feature Selection

针对传统的均值漂移算法,加入了自适应特征选择,提高了均值漂移算法在复杂场景中目标跟踪的鲁棒性。传统的均值漂移算法往往选择固定的一个或两个特征(比如颜色)对目标进行跟踪,当跟踪场景发生变化,容易跟踪失败。本文通过分析被跟踪目标特征与变化背景的区分度来确定最显著特征与次显著特征,从而选择出最有效的目标特征,实现复杂变化场景下的目标跟踪。一系列不同场景下的运动目标跟踪实验,证实了该算法的可靠性。

By self-adaptive feature selection, the traditional mean shift tracking algorithm was improved and its robustness was strengthened for object tracking in the complicated circumstance. Since one or two fixed features （such as the color） were usually selected for object tracking in traditional mean shift tracking algorithm, object tracking would be failure in the changeable circumstance. The remarkable and non-remarkable features were determined respectively by analyzing the distinguishing degree between candidates of object feature tracked and changeable background so that the most effective features were then selected to achieve object tracking in the complicated and changing circumstance. The reliability of the improved algorithm has been verified in serial experimental results of moving object tracking in different circumstance.