DocumentCode :
108859
Title :
Automatic Feeding Control for Dense Aquaculture Fish Tanks
Author :
Atoum, Yousef ; Srivastava, Steven ; Xiaoming Liu
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Volume :
22
Issue :
8
fYear :
2015
fDate :
Aug. 2015
Firstpage :
1089
Lastpage :
1093
Abstract :
This paper introduces an efficient visual signal processing system to continuously control the feeding process of fish in aquaculture tanks. The aim is to improve the production profit in fish farms by controlling the amount of feed at an optimal rate. The automatic feeding control includes two components: 1) a continuous decision on whether the fish are actively consuming feed, and 2) automatic detection of the number of excess feed populated on the water surface of the tank using a two-stage approach. The amount of feed is initially detected using the correlation filer applied to an optimum local region within the video frame, and then followed by a SVM-based refinement classifier to suppress the falsely detected feed. Having both measures allows us to accurately control the feeding process in an automated manner. Experimental results show that our system can accurately and efficiently estimate both measures.
Keywords :
aquaculture; image classification; particle filtering (numerical methods); support vector machines; tanks (containers); SVM-based refinement classifier; automatic feeding control; continuous decision; correlation filer; dense aquaculture fish tanks; fish farms; tank water surface; visual signal processing system; Aquaculture; Computer vision; Correlation; Feature extraction; Feeds; Monitoring; Support vector machines; Bag-of-Words (BoW); HOG; correlation filter (CF); feeding control; fish; particle filter;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2385794
Filename :
6997987
Link To Document :
بازگشت