• KAPE Magazine
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• s.116
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• pp.4-7
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• 2006

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 1998.10a
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• pp.25-37
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• 1998

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.278-283
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• 1999

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.770-772
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• 2015

어미로부터 생후 21일령 또는 28일령에 젖을 때는 이유자돈들만을 개별적인 돈사에서 합사하는 경우, 낯선 환경 및 새로운 동료들과의 서열 구분을 위한 공격적인 행동이 매우 빈번하게 발생한다. 이로 인한 돼지의 성장 저하는 농가의 소득 하락으로 이어져 국내 외 양돈 농가의 큰 문제로 인식되고 있다. 본 논문에서는 키넥트 카메라에서 취득할 수 있는 영상의 깊이정보를 이용하여 이유자돈들의 공격적인 행동을 조기 탐지할 수 있는 프로토타입 모니터링 시스템을 제안한다. 먼저 제안한 시스템은 키넥트의 적외선 센서에서 실시간으로 취득하는 깊이 정보로부터 움직임이 있는 객체들만을 탐지하고, 해당 객체들의 ROI를 설정한다, 둘째, ROI를 이용하여 5가지 특정 정보(객체의 평균, 최고, 최소 속도, 객체 속도의 표준편차, 두 객체 사이의 최소 거리)를 추출한다. 셋째, 취득한 특징 정보는 이진 클래스 분류 문제로 해석하여, 기계학습의 대표적인 모델인 SVM을 탐지기로 사용하였다. 실제 이유자돈사에서 취득한 키넥트 영상을 이용하여 모의 실험을 수행한 결과 안정적인 성능을 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.282-289
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• 2017

Animal Welfare is spreading throughout the world, but remains weak in Korean swine farms. Therefore, the aim of this study was to identify the effect of floor type and hanging type environmental enrichment on the behavior of growing pigs under the traditional feeding environment. A total of 45 crossbred pigs (Yorkshire${\times}$Landrace${\times}$Duroc; average weight, $33.35{\pm}5.5kg$) were assigned into three treatments consisting of control, hanging type(T1) and floor type(T2) with three replicates in semi-slurry pen. The hanging enrichment was suspended at shoulder height of the pigs, and the floor enrichment was fixed in the center of the pens. Growth and cortisol were estimated at the end of the experimental period. Behaviour patterns were analyzed on the first and eighth days after starting the experiment. Growth was not differed among control and treatments. However, cortisol was decreased in T2 compared to control(25.28 vs. 46.75 ng/mL; p<0.05). On the first day, movement and aggression were lower in both treatments than in control(p<0.01). On the eighth day, time and frequency of playing action were increased in T2 compared to T1(p<0.01) and both treatment groups showed more active behaviour than control(p<0.01). These results suggested that the enrichment may meet the natural action requirement of pigs. Therefore, our data can be utilized as basic information for welfare with environmental enrichments in farm animals.

• Journal of Animal Environmental Science
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• v.7 no.3
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• pp.147-154
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• 2001

An experiment was conducted to examine environmental influences upon the behavioral pattern of pigs. The resting areas of an enclosed growing-finishing pig house were checked in two seasonal ventilation systems, and the excretion habit of pigs influenced by the different closing rates (50, 75 and 100%) of side walls of pens was surveyed. 1. The excretion habit of pigs was not influenced by temperature, humidity and the flow speed of running air as they excreted in a fixed area of the side walls. However, the lighting effects on the excretion habit was observed because pigs excreted in the darkest area of the pig pen. 2. The accumulated height and width of feces showed 10 and 30 cm; 5 and 25cm; and 3 and 20cm for 50, 75 and 100% of closing rates of side walls, respectively. It indicates that pigs excrete all over the floor in the pen with 100% closed side walls. 3. Ammonia concentrations of the resting areas on the pen floor were determined to 4.2, 5.1 and $5.8mg/{\ell}$ for 50, 75 and 100% of closing rates of side walls, respectively. It indicates that the ammonia concentration was highest in the pen with 100% closed side walls. Thus, the high ammonia concentration of the resting areas could be reduced by illuminating the darker areas with relation to the excretion habit. 4. The flow speed of running air was likely the biggest factor influencing the resting areas of pigs; pigs took a rest at the place of 0.04 m/s air flow speed point during midwinter, and at the place of 0.24 m/s air flow speed point during midsummer.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.689-692
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• 2016

출입이 없는 폐쇄된 환경에서 객체의 자동 감시 시스템은 객체의 움직임을 추적하여 객체의 지속적인 관찰과 함께 행동 패턴을 효율적으로 분석하기 위해 사용되고 있다. 특히, 국내 돈사의 경우 돈사 내 여러 마리의 돼지들을 개별적으로 관리하기 위해 이러한 감시 시스템은 필수적이다. 그러나 여러마리의 돼지들이 근접하여 개별적으로 추적하기 위해서는 비디오 스트림에서의 매 프레임마다 정확한 분리가 되어야 한다. 본 논문에서는 근접한 돼지의 시퀀스에 분리 알고리즘을 이용하여 매 프레임마다 정확도를 측정한 후 통계적 검정을 통하여 근접한 객체에 대한 최적의 분리 알고리즘을 결정하는 방법을 제안한다. 즉, 시퀀스의 연속된 프레임에서 분리 정확도를 계산하고 통계적 가설 검정을 수행하여 분리 정확도가 일정 수치를 넘지 못하면 다른 분리 알고리즘을 수행하도록 결정한다. 실험 결과, 제안 방법을 이용하여 제안된 가설에 의해 매 프레임마다 최적의 분리 알고리즘을 수행하도록 결정하였다.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.1-10
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• 2018

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• Journal of Animal Environmental Science
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• v.15 no.2
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• pp.131-138
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• 2009

This study was conducted to find ways to control environment with the difference between body temperature and background temperature based on swine activity, and to apply to the environment control system of swine barns based on the findings. Following are the results. 1. Swine activity related to background temperature was achieved as color images and swine activity status was categorized into cold, comfortable, and hot periods with visualization system (thermal image system). 2. Thermal image system consisted of an infrared CCD camera, an image processing board - DIF (TH3100), an main computer (400Hz, 128M, 586 Pentium model) with C++ program installed. 3. Thermal image system categorizing temperatures into cold, comfortable, and hot was applicable to the environment control system of swine barns 4. Feed intake was higher in cold temperature, and finishing weight and weight gain per day in cold temperature were lower than others (p<0.05).