• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.77-86
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• 2018

Although separation of touching pigs in real-time is an important issue for a 24-h pig monitoring system, it is challenging to separate accurately the touching pigs in a crowded pig room. In this study, we propose a separation method for touching pigs using the information generated from Convolutional Neural Network(CNN). Especially, we apply one of the CNN-based object detection methods(i.e., You Look Only Once, YOLO) to solve the touching objects separation problem in an active manner. First, we evaluate and select the bounding boxes generated from YOLO, and then separate touching pigs by analyzing the relations between the selected bounding boxes. Our experimental results show that the proposed method is more effective than widely-used methods for separating touching pigs, in terms of both accuracy and execution time.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.135-144
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• 2018

The domestic pigsty environment is highly vulnerable to the spread of respiratory diseases such as foot-and-mouth disease because of the small space. In order to manage this issue, a variety of studies have been conducted to automatically analyze behavior of individual pigs in a pig pen through a video surveillance system using a camera. Even though it is required to correctly segment touching pigs for tracking each pig in complex situations such as aggressive behavior, detecting the correct boundaries among touching pigs using Kinect's depth information of lower accuracy is a challenging issue. In this paper, we propose a segmentation method using motion information of the touching pigs. In addition, our proposed method can be applied for detecting tracking errors in case of tracking individual pigs in the complex environment. In the experimental results, we confirmed that the touching pigs in a pig farm were separated with the accuracy of 86%, and also confirmed that the tracking errors were detected accurately.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1361-1370
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• 2016

To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for pig monitoring environment, segmenting each pig from touching-pigs is still entrenched as a difficult problem. In this paper, we propose a segmentation method for touching-pigs by using concave-points and edge information in a video surveillance system. Especially, we interpret the segmentation problem as a time-series analysis problem in order to identify the concave-points generated by touching-pigs. Based on the experimental results with the videos obtained from a domestic pig farm, we believe that the proposed method can accurately segment the touching-pigs.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.319-326
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• 2016

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. In this paper, we propose a new Kinect camera-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 moving-pigs are labeled as regions of interest. 3) A contour method is proposed and applied to solve the touching-pigs problem in the Kinect-depth image. The experimental results with the depth videos obtained from a pig farm located in Sejong illustrate the efficiency of the proposed method.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.215-223
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• 2016

To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for livestock monitoring environment, segmenting each pig from touching pigs is still entrenched as a difficult problem. In this paper, we propose a real-time segmentation method for moving pigs by using motion information in a 24-h video surveillance system. The experimental results with the videos obtained from a domestic pig farm illustrated the possibility for segmenting by using our proposed method in real-time.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.905-908
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• 2017

국내 돈사 환경에서 돼지들의 세밀한 관리를 위해, 개별 돼지 관리를 자동화하는 방법이 필요하고, 개별 돼지 관리를 위해서는 근접한 돼지들을 개별 돼지들로 구분이 우선적으로 수행되어져야 한다. 영역 기반의 정보를 사용하여 개별 돼지를 구분하는 기존 방법으로는 복잡한 상황에서 개별 돼지로의 분리가 정확하게 되지 않는 문제가 발생한다. 본 논문에서는 이러한 문제를 해결하기 위한 방법으로 움직임 정보를 활용하여 근접 돼지를 분리하고 추적하는 방법을 제안한다. 이전 프레임의 움직임 정보를 계산하여 현재 돼지의 위치 및 방향을 예측하고, 예측된 돼지의 정보를 사용하여 근접한 돼지를 분리하고 추적한다. 실험 결과, 실제 돈사에서 획득한 근접 돼지 시퀀스에서 근접 돼지의 분리 및 추적이 가능함을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.866-869
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• 2015

감시 카메라 환경에서 돈사 내 개별 돼지들의 행동을 자동으로 관리하는 연구는 효율적인 돈사 관리 측면에서 중요한 이슈로 떠오르고 있다. 그러나 돼지들이 근접해 있을 경우 돼지들을 개별적으로 구분하기 어렵기 때문에 근접한 돼지들을 분리하는 방법이 필요하다. 본 논문에서는 시공간 정보를 이용하여 근접한 돼지를 개별적으로 분리하는 방법을 제안한다. 돈사 내 돼지의 행동 영상 중에서 두 마리의 돼지가 근접한 경우, 돼지가 근접하기 전의 정보와 돼지가 근접한 현재의 정보를 사용하여 새로운 프레임을 생성하고 생성된 프레임에서 돼지의 구분이 명확하지 않은 작은 부분은 영역확장 기법을 이용하여 근접한 돼지를 개별적으로 분리한다. 실험결과, 제안방법을 이용하여 근접한 돼지를 개별적으로 분리할 수 있다는 것을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1428-1430
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• 2015

감시 카메라 환경에서 돈사 내 개별 돼지들의 행동을 자동으로 관리하는 연구는 효율적인 돈사 관리 측면에서 중요한 이슈로 떠오르고 있다. 그러나 움직이는 돼지들이 근접해 있을 경우 돼지들을 개별적으로 구분하기 어렵기 때문에 근접한 돼지들을 분할하는 효과적인 방법이 필요하다. 본 논문에서는 비디오 시퀀스에서의 근접 돼지 분할 문제를 연속 프레임간의 전경 픽셀 정렬 문제로 정형화하여 해결하는 방법을 제안한다. 즉, 돈사에서 top-view로 획득한 영상 중에서 움직이는 돼지들이 근접한 경우, 돼지들의 경계가 구분된 이전 프레임의 정보를 현재 프레임에 투영한다. 이 때 개별 돼지의 움직임이 독립적임을 고려하여 이전 프레임의 개별 돼지 영역을 현재 프레임의 전경 영역에 각각 정렬함으로써 현재 프레임의 근접한 돼지를 개별적으로 분할한다. 실험결과, 제안 방법을 이용하여 3마리 이상의 근접한 돼지를 개별적으로 분할할 수 있음을 확인하였다.