Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs

Stavrakakis, S.; Wei, Li; Guy, Jonathan; Morgan, Graham; Ushaw, Gary; Johnson, Garth; Edwards, Sandra

doi:10.1016/j.compag.2015.07.003

Cited by 55 publications

(35 citation statements)

References 24 publications

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“…Movement of pigs was first captured by using the Vicon 3D optoelectronic motion analysis system to detect the characteristic locomotory changes of lame pigs (Stavrakakis et al, 2015a). This system was then compared with the Kinect sensor to distinguish sound and lame pigs by Stavrakakis et al (2015b). Reflective markers were attached at the central nasal bone, the mid-neck proximal to shoulders (frontal to the shoulder widening), the posterior mid-thorax, anterior mid-pelvis and tail base of pigs.…”

Section: Locomotion and Lameness Behaviourmentioning

confidence: 99%

Implementation of machine vision for detecting behaviour of cattle and pigs

2017

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Section: Locomotion and Lameness Behaviourmentioning

confidence: 99%

Implementation of machine vision for detecting behaviour of cattle and pigs

2017

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“…More practical solutions for a commercial environment use a single camera to measure lameness in 3D (Stavrakakis et al., 2015) and the height of pigs (i.e., lying down; Kulikov et al., 2014). …”

Section: Sensors For Measuring Pig Behaviourmentioning

confidence: 99%

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Matthews

Miller

Clapp

et al. 2016

The Veterinary Journal

195

129

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HighlightsRecent developments in automatically detecting compromised pig health and welfare.Five categories of behaviour are reviewed.Behaviours mapped to sensors that are feasible for automated detection.Progress towards levels of automation through detection, monitoring and fully automatic detection of behavioural change.Challenges for automated detection of behavioural changes are multifaceted and require trade-offs to develop such systems.

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“…For previous research on identification, References [11,16-18, 20-22,24-26,29,31,38,44-48] for detection and References [10,19,37,39,45] for tracking exist. In addition, previous studies for early detection of abnormalities exist as various topics, including research on the movement of pigs [17,62], research on aggressive behavior of pigs [63,64], research on attitude change [16,22,23,31,32,34,35,40,46], research on mounting behavior [21], research on low-growth pig's behavior [49], research on pig weight [29,33,38] and research on the density of pigs [9,11].…”

Section: Introductionmentioning

confidence: 99%

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

et al. 2020

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Automated pig monitoring is an important issue in the surveillance environment of a pig farm. For a large-scale pig farm in particular, practical issues such as monitoring cost should be considered but such consideration based on low-cost embedded boards has not yet been reported. Since low-cost embedded boards have more limited computing power than typical PCs and have tradeoffs between execution speed and accuracy, achieving fast and accurate detection of individual pigs for “on-device” pig monitoring applications is very challenging. Therefore, in this paper, we propose a method for the fast detection of individual pigs by reducing the computational workload of 3 × 3 convolution in widely-used, deep learning-based object detectors. Then, in order to recover the accuracy of the “light-weight” deep learning-based object detector, we generate a three-channel composite image as its input image, through “simple” image preprocessing techniques. Our experimental results on an NVIDIA Jetson Nano embedded board show that the proposed method can improve the integrated performance of both execution speed and accuracy of widely-used, deep learning-based object detectors, by a factor of up to 8.7.

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Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs

Cited by 55 publications

References 24 publications

Implementation of machine vision for detecting behaviour of cattle and pigs

Implementation of machine vision for detecting behaviour of cattle and pigs

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

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