NDLI: Multi-target tracking-based detection of floc settling velocity

Content Provider	IEEE Xplore Digital Library
Author	Lu Minggang Sun Yi Tang Liang
Copyright Year	2011
Description	Author affiliation: Department of School of Mechatronics Engineering and Automation, University of Shanghai, 200072, China (Lu Minggang; Sun Yi; Tang Liang)
Abstract	In the automatic control of coagulant dosage in water plant, the final control performance mainly depends on the chosen of control parameters. In this paper, a multi-target tracking-based detection system for floc settling velocity is proposed. The system collects floc dynamic change images online by using an underwater image sensor, and transmits the images to computer image processing system. Therefore the flocs characteristics and their settling velocities will be calculated through image processing and multi-target tracking. Finally, extraction and analysis of floc feature characteristics can be achieved. The system uses Kalman filter and multiple hypothesis tracking (MHT) algorithms to calculate the flocs settling velocities. Weighted average floc settling velocity can be calculated by weighted mathematical statistics. Experimental results show that using of detection system can reflect the floc sedimentation status underwater, and average floc settling velocity is within 0.08 ∼ 0.140 mm/s. This system provides an effective control parameter, floc settling velocity, for the automatic control of coagulant dosage.
Starting Page	72
Ending Page	76
File Size	227809
Page Count	5
File Format	PDF
ISBN	9781424481583
e-ISBN	9781424481613
DOI	10.1109/ICEMI.2011.6037950
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis image processing Target tracking multi-target tracking speed of sit Educational institutions flocculation detection machine vision controlled parameters water treatment Prediction algorithms Water pollution Testing
Content Type	Text
Resource Type	Article

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