基于自适应特征融合的粒子滤波目标跟踪算法

Particle Filter Object Tracking Based on Adaptive Feature Fusion

提出了一种基于自适应特征融合的粒子滤波跟踪算法,用于解决传统的粒子滤波跟踪方法在复杂背景下容易跟踪失败的问题。该算法选取颜色特征和边缘特征来描述目标,并通过粒子滤波进行特征融合,根据可靠性因子调整各特征的权值系数;在跟踪过程中,随着目标自身形变,自适应更新目标模板。实验结果表明,在复杂背景下以及受到遮挡时,本算法能够准确稳健地跟踪目标。

In order to solve the problem that the traditional particle filter tracking method fails under the complex background,a tracking algorithm named particle filter object tracking based on feature fusion adaptively was proposed.The object is represented by color feature and edge feature that are selected in our algorithm and fused by the particle filter.The weighting coefficient of feature is adjusted by the reliability.If the scale of object is changing,the template of object in the tracking process is needed to update.The experimental results show that the proposed algorithm can track the object stably and more accurately when the object is under the complex background or is occluded.