{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:30:15Z","timestamp":1764174615640,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T00:00:00Z","timestamp":1550620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The fast detection of pigs is a crucial aspect for a surveillance environment intended for the ultimate purpose of the 24 h tracking of individual pigs. Particularly, in a realistic pig farm environment, one should consider various illumination conditions such as sunlight, but such consideration has not been reported yet. We propose a fast method to detect pigs under various illumination conditions by exploiting the complementary information from depth and infrared images. By applying spatiotemporal interpolation, we first remove the noises caused by sunlight. Then, we carefully analyze the characteristics of both the depth and infrared information and detect pigs using only simple image processing techniques. Rather than exploiting highly time-consuming techniques, such as frequency-, optimization-, or deep learning-based detections, our image processing-based method can guarantee a fast execution time for the final goal, i.e., intelligent pig monitoring applications. In the experimental results, pigs could be detected effectively through the proposed method for both accuracy (i.e., 0.79) and execution time (i.e., 8.71 ms), even with various illumination conditions.<\/jats:p>","DOI":"10.3390\/sym11020266","type":"journal-article","created":{"date-parts":[[2019,2,20]],"date-time":"2019-02-20T11:45:39Z","timestamp":1550663139000},"page":"266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Fast Pig Detection with a Top-View Camera under Various Illumination Conditions"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6470-3341","authenticated-orcid":false,"given":"Jaewon","family":"Sa","sequence":"first","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}]},{"given":"Younchang","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}]},{"given":"Hanhaesol","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Convergence Software, Korea University, Sejong 30019, Korea"}]},{"given":"Jinho","family":"Cho","sequence":"additional","affiliation":[{"name":"Division of Food and Animal Science, Chungbuk National University, Cheongju 28644, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,20]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Precision Livestock Farming: An International Review of Scientific and Commercial Aspects","volume":"5","author":"Banhazi","year":"2012","journal-title":"Int. J. Agric. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.sbsr.2016.11.004","article-title":"Recent Advances in Wearable Sensors for Animal Health Management","volume":"12","author":"Neethirajan","year":"2017","journal-title":"Sens. Bio-Sens. Res."},{"key":"ref_3","unstructured":"Tullo, E., Fontana, I., and Guarino, M. (2013, January 10\u201312). Precision Livestock Farming: An Overview of Image and Sound Labelling. Proceedings of the 6th European Conference on Precision Livestock Farming (EC-PLF 2013), Leuven, Belgium."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.tvjl.2016.09.005","article-title":"Early Detection of Health and Welfare Compromises through Automated Detection of Behavioural Changes in Pigs","volume":"217","author":"Matthews","year":"2016","journal-title":"Vet. J."},{"key":"ref_5","first-page":"23","article-title":"A Brief Review of the Application of Machine Vision in Livestock Behaviour Analysis","volume":"7","author":"Tscharke","year":"2016","journal-title":"J. Agric. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Han, S., Zhang, J., Zhu, M., Wu, J., and Kong, F. (2017, January 26\u201328). Review of Automatic Detection of Pig Behaviours by using Image Analysis. Proceedings of the International Conference on AEECE, Chengdu, China.","DOI":"10.1088\/1755-1315\/69\/1\/012096"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1017\/S1751731117001239","article-title":"The Automated Analysis of Clustering Behaviour of Piglets from Thermal Images in response to Immune Challenge by Vaccination","volume":"12","author":"Cook","year":"2018","journal-title":"Animal"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compag.2018.06.043","article-title":"Model-based Detection of Pigs in Images under Sub-Optimal Conditions","volume":"152","author":"Brunger","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12407","DOI":"10.3390\/s150921407","article-title":"Illumination and Reflectance Estimation with its Application in Foreground","volume":"15","author":"Tu","year":"2015","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1049\/iet-ipr.2012.0734","article-title":"Segmentation of Sows in Farrowing Pens","volume":"8","author":"Tu","year":"2014","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.biosystemseng.2013.06.011","article-title":"Foreground Detection using Loopy Belief Propagation","volume":"116","author":"Tu","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1017\/S1751731115001342","article-title":"Development of Automatic Surveillance of Animal Behaviour and Welfare using Image Analysis and Machine Learned Segmentation Techniques","volume":"9","author":"Nilsson","year":"2015","journal-title":"Animal"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.livsci.2013.11.007","article-title":"Automatic Monitoring of Pig Locomotion using Image Analysis","volume":"159","author":"Kashiha","year":"2014","journal-title":"Livest. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.biosystemseng.2016.08.018","article-title":"Automatic Estimation of Number of Piglets in a Pen during Farrowing, using Image Analysis","volume":"151","author":"Oczak","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.compag.2011.01.011","article-title":"Development of a Real-Time Computer Vision System for Tracking Loose-Housed Pigs","volume":"76","author":"Ahrendt","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","first-page":"73","article-title":"Real-Time Recognition of Sows in Video: A Supervised Approach","volume":"1","author":"Khoramshahi","year":"2014","journal-title":"Inf. Process. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.compag.2016.04.022","article-title":"Automatic Detection of Mounting Behaviours among Pigs using Image Analysis","volume":"124","author":"Nasirahmadi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1017\/S1751731116001208","article-title":"A New Approach for Categorizing Pig Lying Behaviour based on a Delaunay Triangulation Method","volume":"11","author":"Nasirahmadi","year":"2017","journal-title":"Animal"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.applanim.2017.06.015","article-title":"Using Automated Image Analysis in Pig Behavioural Research: Assessment of the Influence of Enrichment Subtrate Provision on Lying Behaviour","volume":"196","author":"Nasirahmadi","year":"2017","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5424\/sjar\/2009073-438","article-title":"An Automatic Colour-based Computer Vision Algorithm for Tracking the Position of Piglets","volume":"7","author":"Gomez","year":"2009","journal-title":"Span. J. Agric. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.biosystemseng.2014.07.002","article-title":"Foreground Detection of Group-Housed Pigs based on the Combination of Mixture of Gaussians using Prediction Mechanism and Threshold Segmentation","volume":"125","author":"Guo","year":"2014","journal-title":"Biosyst. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.biosystemseng.2015.05.001","article-title":"Multi-Object Extraction from Topview Group-Housed Pig Images based on Adaptive Partitioning and Multilevel Thresholding Segmentation","volume":"135","author":"Guo","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_23","first-page":"145","article-title":"Boundary Detection of Pigs in Pens based on Adaptive Thresholding using an Integral Image and Adaptive Partitioning","volume":"16","author":"Buayai","year":"2017","journal-title":"CMU J. Nat. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compag.2015.11.008","article-title":"An Automatic Splitting Method for the Adhesive Piglets Gray Scale Image based on the Ellipse Shape Feature","volume":"120","author":"Lu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.compag.2018.10.030","article-title":"An Automatic Ear Base Temperature Extraction Method for Top View Piglet Thermal Image","volume":"155","author":"Lu","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.compag.2018.08.006","article-title":"Estimating Pig Weights from Images without Constraint on Posture and Illumination","volume":"153","author":"Jun","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108876","DOI":"10.1155\/2018\/1083876","article-title":"A Multiobjective Piglet Image Segmentation Method based on an Improved Noninteractive GrabCut Algorithm","volume":"2018","author":"Kang","year":"2018","journal-title":"Adv. Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.biosystemseng.2018.09.011","article-title":"Automatic Recognition of Sow Nursing Behavious using Deep Learning-based Segmentation and Spatial and Temporal Features","volume":"175","author":"Yang","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.compag.2018.11.002","article-title":"Feeding Behavior Recognition for Group-Housed Pigs with the Faster R-CNN","volume":"155","author":"Yang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.compag.2014.08.008","article-title":"Estimation of Pig Weight using a Microsoft Kinect Prototype Imaging System","volume":"109","author":"Kongsro","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.compag.2016.04.026","article-title":"Automatic Recognition of Lactating Sow Behaviors through Depth Image Processing","volume":"125","author":"Lao","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2015.07.003","article-title":"Validity of the Microsoft Kinect Sensor for Assessment of Normal Walking Patterns in Pigs","volume":"117","author":"Stavrakakis","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Ren, J., Barclay, D., McCormack, S., and Thomson, W. (2015, January 26\u201328). Automatic Animal Detection from Kinect Sensed Images for Livestock Monitoring and Assessment. Proceedings of the ICCCIT, Liverpool, UK.","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.172"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.jneumeth.2014.07.012","article-title":"Application of 3D Imaging Sensor for Tracking Minipigs in the Open Field Test","volume":"235","author":"Kulikov","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.compag.2016.08.012","article-title":"An Approach of Pig Weight Estimation using Binocular Stereo System based on LabVIEW","volume":"129","author":"Shi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"17582","DOI":"10.1038\/s41598-017-17451-6","article-title":"Automated Tracking to Measure Behavioural Changes in Pigs for Health and Welfare Monitoring","volume":"7","author":"Matthews","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.compag.2018.01.023","article-title":"Automatic Recognition of Lactating Sow Postures from Depth Images by Deep Learning Detector","volume":"147","author":"Zheng","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lee, J., Jin, L., Park, D., and Chung, Y. (2016). Automatic Recognition of Aggressive Pig Behaviors using Kinect Depth Sensor. Sensors, 16.","DOI":"10.3390\/s16050631"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kim, J., Chung, Y., Choi, Y., Sa, J., Kim, H., Chung, Y., Park, D., and Kim, H. (2017). Depth-based Detection of Standing-Pigs in Moving Noise Environments. Sensors, 17.","DOI":"10.3390\/s17122757"},{"key":"ref_40","first-page":"1481","article-title":"A Cost-Effective Pigsty Monitoring System based on a Video Sensor","volume":"8","author":"Chung","year":"2014","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ju, M., Choi, Y., Seo, J., Sa, J., Lee, S., Chung, Y., and Park, D. (2018). A Kinect-based Segmentation of Touching-Pigs for Real-Time Monitoring. Sensors, 18.","DOI":"10.3390\/s18061746"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zuo, S., Jin, L., Chung, Y., and Park, D. (2014, January 1\u20132). An Index Algorithm for Tracking Pigs in Pigsty. Proceedings of the ICITMS, Hong Kong, China.","DOI":"10.2495\/ICIEE140931"},{"key":"ref_43","unstructured":"(2018, February 28). Intel RealSense D435, Intel. Available online: https:\/\/click.intel.com\/intelr-realsensetm-depth-camera-d435.html."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1109\/JSEN.2014.2309987","article-title":"Characterization of Noise in Kinect Depth Images: A Review","volume":"14","author":"Mallick","year":"2014","journal-title":"IEEE Sens. J."},{"key":"ref_45","first-page":"1","article-title":"Efficient Medical Image Enhancement using CLAHE and Wavelet Fusion","volume":"167","author":"Singh","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_46","unstructured":"Eramian, M., and Mould, D. (2005, January 9\u201311). Histogram Equalization using Neighborhood Metrics. Proceedings of the 2nd Canadian Conference on Computer and Robot Vision (CRV\u201905), Victoria, BC, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_48","unstructured":"Nadimi, S., and Bhanu, B. (2003, January 1). Physics-based Models of Color and IR Video for Sensor Fusion. Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI\u201903), Tokyo, Japan."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Becker, S., Scherer-Negenborn, N., Thakkar, P., H\u00fcbner, W., and Arens, M. (2016, January 26\u201327). The effects of camera jitter for background subtraction algorithms on fused infrared-visible video streams. Proceedings of the Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, Edinburgh, UK.","DOI":"10.1117\/12.2239884"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1109\/TCSVT.2017.2721460","article-title":"Fast Grayscale-Thermal Foreground Detection with Collaborative Low-rank Decomposition","volume":"28","author":"Yang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.cosrev.2018.01.004","article-title":"On the Role and the Importance of Features for Background Modeling and Foreground Detection","volume":"28","author":"Bouwmans","year":"2018","journal-title":"Comput. Sci. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Maddalena, L., and Petrosino, A. (2018). Background Subtraction for Moving Object Detection in RGB-D Data: A Survey. J. Imaging, 4.","DOI":"10.3390\/jimaging4050071"},{"key":"ref_53","unstructured":"(2016, December 18). Open Source Computer Vision, OpenCV. Available online: http:\/\/opencv.org."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2016). YOLO9000: Better, Faster, Stronger, arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, C., and Ross, A. (2018, January 15). A Multi-Task Convolutional Neural Network for Joint Iris Detection and Presentation Attack Detection. Proceedings of the 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACVW.2018.00011"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on CVPR, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Buehler, M., Iagnemma, K., and Singh, S. (2009). The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, Springer.","DOI":"10.1007\/978-3-642-03991-1"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TCSVT.2005.846433","article-title":"Power-Rate-Distortion Analysis for Wireless Video Communication under Energy Constraint","volume":"15","author":"He","year":"2005","journal-title":"IEEE Trans. Syst. Video Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MCAS.2008.923007","article-title":"Energy-Aware Portable Video Communication System Design for Wildlife Activity Monitoring","volume":"8","author":"He","year":"2008","journal-title":"IEEE Circuits Syst. Mag."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/2\/266\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:33:29Z","timestamp":1760186009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/2\/266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,20]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["sym11020266"],"URL":"https:\/\/doi.org\/10.3390\/sym11020266","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,2,20]]}}}