Machine vision application in animal trajectory tracking. Issue 127 (April 2016)

• Record Type: Journal Article
• Authors: Koniar, Dušan; Hargaš, Libor; Loncová, Zuzana; Duchoň, František; Beňo, Peter
• Is Part Of: Computer methods and programs in biomedicine. Issue 127(2016)
• Page Start: 258
• Page End: 272
• Publication Date: 2016-04
• Subjects: Animal tracking -- Differential methods -- Thresholding-based methods -- Color matching; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.12.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Five methods used for animal trajectory tracking, which were proposed with the usage of visual system. All the algorithms are proposed for poor-quality images (non-homogenous lightning, presence of grid, etc.). All methods are compared and their effectiveness is evaluated. Proposed methods are compared with standard used methods for animal tracking as GPS tracking or RFID tracking. They bring several advantages such as user-friendly and simple interface appropriate for medical (non-technical) specialists, too. Abstract: This article was motivated by the doctors' demand to make a technical support in pathologies of gastrointestinal tract research[10], which would be based on machine vision tools. Proposed solution should be less expensive alternative to already existing RF (radio frequency) methods. The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology (gastric ulcer). In the theoretical part of the article, several methods of animal trajectory tracking are presented: two differential methods based on background subtraction, the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors. The methods were tested offline on five video samples. Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions.