Monitoring of motor vehicle exhaust emissions using Gaussian process regression frame interpolation optical flow...

Area of Science:

• Environmental Science
• Fluid Dynamics
• Optical Engineering

Background:

• Infrared cameras in fluid pollutant monitoring face challenges with low frame rates, disrupting pixel motion continuity.
• This disruption leads to significant errors in capturing pollutant distribution and evolution, hindering accurate monitoring.

Purpose of the Study:

• To introduce a novel method, Gaussian Process Regression Frame Interpolation Optical Flow (GPR-FIOF), to restore spatial continuity of pixel motion.
• To enhance the precision of fluid pollutant motion estimation and improve monitoring accuracy.

Main Methods:

• Development and application of the Gaussian Process Regression Frame Interpolation Optical Flow (GPR-FIOF) algorithm.
• Validation through fluid simulations and field experiments using infrared gas correlation spectroscopy.

Main Results:

• GPR-FIOF demonstrated significant improvements in accuracy (80.30%) and stability (66.39%) in fluid simulations compared to conventional methods.
• Field experiments showed enhanced accuracy (18.24%) and stability (61.77%) in emission rate inversion.

Conclusions:

• GPR-FIOF effectively mitigates spatial continuity disruption in pixel motion for fluid pollutant monitoring.
• The method bolsters the accuracy and feasibility of environmental monitoring applications for pollutant gas emissions.