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Related Experiment Video

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Gray consistency optical flow algorithm based on mask-R-CNN and a spatial filter for velocity calculation.

Donghua Zhao, Yicheng Wu, Chenguang Wang

|February 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel optical flow method using Mask-R-CNN and pyramid Lucas-Kanade (LK) algorithm to accurately measure vehicle velocity by mitigating interference from moving objects on the ground.

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Area of Science:

Computer Vision
Robotics
Automotive Engineering

Background:

Optical flow methods are standard for vehicle velocity measurement using downward-facing cameras.
Moving objects on the ground generate interfering optical flow, reducing measurement accuracy.

Purpose of the Study:

To enhance vehicle velocity measurement accuracy by addressing interference from moving objects.
To leverage color image information and improve the robustness of optical flow calculations.

Main Methods:

Integration of Mask-R-CNN for moving object detection and masking.
Application of pyramid Lucas-Kanade (LK) algorithm with gray consistency on RGB image channels.
Median filtering applied to R, G, and B image channels.

Main Results:

Significantly increased accuracy in vehicle velocity measurement relative to the ground.
Reduced fluctuation in velocity calculations by thoroughly utilizing color image data.
Minimized erroneous values caused by image noise and similar color masks.

Conclusions:

The proposed algorithm effectively enhances vehicle velocity measurement accuracy.
The method demonstrates superior precision and reliability in experimental validation.
This approach offers a robust solution for accurate vehicle velocity estimation in complex environments.