TY - JOUR
T1 - High-Precision Visual-Tracking using the IMM Algorithm and Discrete GPI Observers (IMM-DGPIO)
T2 - Categories (4)(7)
AU - Sánchez-Ramírez, Edwards Ernesto
AU - Rosales-Silva, Alberto Jorge
AU - Alfaro-Flores, Rogelio Antonio
N1 - Publisher Copyright:
© 2020, Springer Nature B.V.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - GPI observer
KW - High-precision
KW - Interacting multiple models
KW - Real-time
KW - Visual tracking
UR - http://www.scopus.com/inward/record.url?scp=85084128647&partnerID=8YFLogxK
U2 - 10.1007/s10846-020-01164-6
DO - 10.1007/s10846-020-01164-6
M3 - Artículo
SN - 0921-0296
VL - 99
SP - 815
EP - 835
JO - Journal of Intelligent and Robotic Systems: Theory and Applications
JF - Journal of Intelligent and Robotic Systems: Theory and Applications
IS - 3-4
ER -