改进D-S证据理论在变电站人体跌倒检测的应用

为了提高变电站人体跌倒检测准确率,提出了一种基于改进D-S证据理论的人体跌倒检测算法。利用垂直外接矩形和最小面积外接矩形对检测到的人体目标进行描述,分析目标区域的矩形宽高比、人体质心高度比和人体躯干倾斜角的人体目标特征变化。针对复杂人体姿态情况下存在的目标特征冲突问题,提出采用D-S证据理论对特征信息进行融合。通过自定义的广义三角模糊函数构造3种人体特征的基本概率指派函数,生成3种人体目标特征基本概率指派(basic probability assignment,BPA)。基于Murphy算法,提出一种双重加权平均证据源的改进算法,既可以融合各个独立证据的一致信息,也可以融合冲突信息。实验结果证明,该人体跌倒检测算法具有较高的跌倒识别准确率,可以合理的生成基本概率指派,有效地融合冲突证据,能够满足变电站人员安全监控的需要。

Application of improved D S evidence theory in human fall detection of transformer substation

To improve the fall detection accuracy in substation, a human fall detection algorithm based on improved D-S evidence theory was proposed in this paper.Human minimum area external rectangle and vertical external rectangle were used to describe the human and the characteristics of rectangle ratio, centroid height ratio and inclination angle of object region were analyzed.The characteristic conflicts in the complex situations can be resolved by using D-S evidence theory to combine the characteristic information.The basic probability assignments of three characteristics were created by using self-defined generalized triangular fuzzy function.An improved method Dual Weighted Average Evidence (DWAE) based on Murphy algorithm was proposed to merge the different independent evidence effectively.The experimental results show that the proposed method has high detection accuracy by generating BPA reasonably and combining the evidences effectively which can be integrated in the security monitoring system of transformer substation.