Enhancing the Performance of a Rainfall Measurement System Using Artificial Neural Networks Based Object Tracking Algorithms

With the recent development of optical sensing and digital image processing techniques, high-speed cameras have been applied to measure the microphysical properties of raindrops. However, the performance of such systems are significantly affected by object tracking algorithms. In order to improve th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE International Instrumentation and Measurement Technology Conference (Online) pp. 1 - 4
Main Authors: Chen, Chih-Yen, Wang, Lijuan, Hwang, Chi-Hung, Hsieh, Chi-Wen, Chi, Po-Wei
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2019
Subjects:
artificial neural network
Artificial neural networks
Atmospheric measurements
Cameras
disdrometer
high-speed camera
Object tracking
object tracking algorithm
Optical variables measurement
rainfall measurement
Sea measurements
Trajectory
ISSN:2642-2077
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the recent development of optical sensing and digital image processing techniques, high-speed cameras have been applied to measure the microphysical properties of raindrops. However, the performance of such systems are significantly affected by object tracking algorithms. In order to improve the measurement accuracy of rainfall rate and accumulated rainfall, a novel object tracking algorithm based on artificial neural networks (ANN) is proposed in this paper. The ANN model takes the features of each raindrop in the two successive images as inputs including the center coordinates, area, canting angle, the lengths of long axis and minor axis of the equivalent ellipse. The output of the ANN model is the matched probabilities of each pair of raindrops between before and after images. Experimental data were collected during a real rainfall event. Performance comparisons between the traditional and ANN based object tracking algorithms are conducted based on the experimental data. Experimental results suggest the successful matching rate is significantly increased from 87.20% to 95.60% due to the usage of the ANN based algorithm. Hence, the improved disdrometer system is capable of producing more accurate and robust measurements of rainfall status.
ISSN:2642-2077
DOI:10.1109/I2MTC.2019.8827108