In this work, we propose the integration of a bank of Discrete Generalized Proportional Integral Observers (DGPIO) within an Interacting Multiple Model (IMM) structure in order to improve the precision of visual-tracking tasks. Applications such as visual servoing, robotic assisted surgery and optronic weapon systems require accurate and high-precision measurements provided by real-time visual-tracking systems. In this case, the DGPIO-Bank was designed using two kinematic models based in constant velocity (CV) and constant acceleration (CA) motion profiles. The main feature of the DGPIO-Bank is the active disturbance rejection (ADR) feature which reduces noise in the position signal of a moving object. The resultant algorithm uses a fusion of four important features: state interaction, Kalman filtering, active disturbance rejection and multiple models combination. For performance comparison, we evaluated our proposed IMM-DGPIO algorithm and other state of the art IMM algorithms. Experimental results show that our proposed strategy had the best performance.

在这项工作中，我们建议在交互式多模型（IMM）结构中集成一组离散广义比例积分观测器（DGPIO），以提高视觉跟踪任务的精度。视觉伺服，机器人辅助手术和光电武器系统等应用需要实时视觉跟踪系统提供准确而高精度的测量。在这种情况下，DGPIO-Bank是使用基于恒定速度（CV）和恒定加速度（CA）运动曲线的两个运动学模型设计的。DGPIO-Bank的主要功能是主动抗扰性（ADR）功能，该功能可减少移动物体的位置信号中的噪声。最终的算法融合了四个重要功能：状态交互，卡尔曼滤波，主动干扰抑制和多种模型的组合。为了进行性能比较，我们评估了我们提出的IMM-DGPIO算法和其他最新的IMM算法。实验结果表明，本文提出的策略具有最佳的性能。