Feasibility study for the implementation of an automatic system for the detection of social interactions in the waiting area of automatic milking stations by using a video surveillance system

Implementation of machine vision for detecting behaviour of cattle and pigs

“…Abdul Jabbar et al, 2017 Aggressive behaviour 2D (CCD camera) Based on geometric features between animals. Guzhva et al, 2016 Mounting behaviour 2D (CCD camera) Based on motion detection and length of moving animals.…”

“…A CCD based method was applied to monitor interactions (i.e. body pushing, head butting, head pressing, body sniffing) between dairy cows (Guzhva et al, 2016). Geometric features (distances) were extracted from every pair of cows then the values were used as inputs of a SVM, with a detection accuracy of around 85%.…”

Welfare Quality®for dairy cows: towards a sensor-based assessment

“…Although social behaviour has not been a priority topic for PLF researchers, some developments can be found in literature. Guzhva et al (2016) used top-view cameras in the waiting area of automated milking systems, together with machine learning algorithms, to automatically detect social interactions (head pressing and body pushing). Moreover, proximity loggers and location solutions have been used to characterise social network structure of dairy herds, detecting positive social behaviours such as grooming (Boyland et al, 2016).…”

Technical note: Using an electronic drinker to monitor competition in dairy cows

“…For example, in agricultural systems, radio frequency monitoring systems can accurately record feeding frequency, duration, and intake (see Chizzotti et al, 2015). Data from automatic systems have also been used to make inferences about social interactions (see Guzhva et al, 2016). Social competition is often assessed during feeding by recording displacements, typically defined as when physical contact from 1 cow (actor) results in the recipient cow (reactor) withdrawing from the feed bunk (DeVries et al, 2004).…”