基于动态贝叶斯网络的多特征目标跟踪

摘要

In this paper,the dynamic Bayesian network model is applied for the tracking of human,and a multi-feature fusion tracking algorithm is proposed.This algorithm firstly establishes the state model based on the Dynamic Bayesian Network, and then extracts the features of body color and gradient under the three different cases,I.e.,deformation,occlusion,and interference, respectively.Both the color feature and gradient feature are integrated by using particle filter method.The experiment results show that the proposed multi-feature algorithm is of better robust and accurate in the problem of human tracking, compared with the traditional single feature in complex environment.%通过将动态贝叶斯网络模型应用到人体目标跟踪中,提出了一种多特征融合跟踪算法.该方法基于动态贝叶斯网络建立状态模型,分别针对形变、遮挡、有干扰三种情况提取运动中人体的颜色和梯度特征,利用粒子滤波方法对颜色特征和梯度特征进行融合.实验表明,提出的多特征跟踪算法能较好地解决复杂环境下的目标跟踪问题,相比传统的利用单一目标特征的跟踪算法具有更好的鲁棒性和准确性.