{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:51:39Z","timestamp":1769640699229,"version":"3.49.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>We present a bio-inspired fish simulation platform, which we call \"Foids\", to generate realistic synthetic datasets for an use in computer vision algorithm training. This is a first-of-its-kind synthetic dataset platform for fish, which generates all the 3D scenes just with a simulation. One of the major challenges in deep learning based computer vision is the preparation of the annotated dataset. It is already hard to collect a good quality video dataset with enough variations; moreover, it is a painful process to annotate a sufficiently large video dataset frame by frame. This is especially true when it comes to a fish dataset because it is difficult to set up a camera underwater and the number of fish (target objects) in the scene can range up to 30,000 in a fish cage on a fish farm. All of these fish need to be annotated with labels such as a bounding box or silhouette, which can take hours to complete manually, even for only a few minutes of video. We solve this challenge by introducing a realistic synthetic dataset generation platform that incorporates details of biology and ecology studied in the aquaculture field. Because it is a simulated scene, it is easy to generate the scene data with annotation labels from the 3D mesh geometry data and transformation matrix. To this end, we develop an automated fish counting system utilizing the part of synthetic dataset that shows comparable counting accuracy to human eyes, which reduces the time compared to the manual process, and reduces physical injuries sustained by the fish.<\/jats:p>","DOI":"10.1145\/3478513.3480520","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:28:45Z","timestamp":1639160925000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Foids"],"prefix":"10.1145","volume":"40","author":[{"given":"Yuko","family":"Ishiwaka","sequence":"first","affiliation":[{"name":"SoftBank Corp., Japan"}]},{"given":"Xiao S.","family":"Zeng","sequence":"additional","affiliation":[{"name":"NeuralX Inc."}]},{"given":"Michael Lee","family":"Eastman","sequence":"additional","affiliation":[{"name":"SoftBank Corp., Japan"}]},{"given":"Sho","family":"Kakazu","sequence":"additional","affiliation":[{"name":"SoftBank Corp., Japan"}]},{"given":"Sarah","family":"Gross","sequence":"additional","affiliation":[{"name":"NeuralX Inc."}]},{"given":"Ryosuke","family":"Mizutani","sequence":"additional","affiliation":[{"name":"Nosan Corporation, Japan"}]},{"given":"Masaki","family":"Nakada","sequence":"additional","affiliation":[{"name":"NeuralX Inc."}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/rog.20022"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.25845\/5e28f062c5097"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jphysparis.2004.03.015"},{"key":"e_1_2_2_4_1","volume-title":"Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934","author":"Bochkovskiy Alexey","year":"2020","unstructured":"Alexey Bochkovskiy , Chien-Yao Wang , and Hong-Yuan Mark Liao . 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 ( 2020 ). Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2007.01941.x"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1210280"},{"key":"e_1_2_2_7_1","volume-title":"Proceedings of the fifth Conference on Fish Telemetry held in Europe","author":"Cubitt KF","year":"2003","unstructured":"KF Cubitt , S Churchill , D Rowsell , DA Scruton , RS McKinley , 2003 . 3-dimensional positioning of salmon in commercial sea cages: assessment of a tool for monitoring behaviour. In Aquatic telemetry. Advances and applications . Proceedings of the fifth Conference on Fish Telemetry held in Europe , Ustica, Italy. 25--33. KF Cubitt, S Churchill, D Rowsell, DA Scruton, RS McKinley, et al. 2003. 3-dimensional positioning of salmon in commercial sea cages: assessment of a tool for monitoring behaviour. In Aquatic telemetry. Advances and applications. Proceedings of the fifth Conference on Fish Telemetry held in Europe, Ustica, Italy. 25--33."},{"key":"e_1_2_2_8_1","doi-asserted-by":"crossref","unstructured":"C\u00e9sar Roberto de Souza Adrien Gaidon Yohann Cabon and Antonio Manuel L\u00f3pez Pe\u00f1a. 2017. Procedural Generation of Videos to Train Deep Action Recognition Networks. arXiv:1612.00881 [cs.CV]  C\u00e9sar Roberto de Souza Adrien Gaidon Yohann Cabon and Antonio Manuel L\u00f3pez Pe\u00f1a. 2017. Procedural Generation of Videos to Train Deep Action Recognition Networks. arXiv:1612.00881 [cs.CV]","DOI":"10.1109\/CVPR.2017.278"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1594\/PANGAEA.926930"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.199.1.83"},{"key":"e_1_2_2_11_1","volume-title":"CARLA: An Open Urban Driving Simulator. arXiv:1711.03938 [cs.LG]","author":"Dosovitskiy Alexey","year":"2017","unstructured":"Alexey Dosovitskiy , German Ros , Felipe Codevilla , Antonio Lopez , and Vladlen Koltun . 2017 . CARLA: An Open Urban Driving Simulator. arXiv:1711.03938 [cs.LG] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. 2017. CARLA: An Open Urban Driving Simulator. arXiv:1711.03938 [cs.LG]"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/0044-8486(94)00384-Z"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1051\/alr\/2012007"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aquaculture.2008.11.031"},{"key":"e_1_2_2_15_1","volume-title":"Gunnar Blohm, and Nikolaus F. Troje.","author":"Ghorbani Saeed","year":"2020","unstructured":"Saeed Ghorbani , Kimia Mahdaviani , Anne Thaler , Konrad Kording , Douglas James Cook , Gunnar Blohm, and Nikolaus F. Troje. 2020 . MoVi: A Large Multipurpose Motion and Video Dataset . arXiv:2003.01888 [cs.CV] Saeed Ghorbani, Kimia Mahdaviani, Anne Thaler, Konrad Kording, Douglas James Cook, Gunnar Blohm, and Nikolaus F. Troje. 2020. MoVi: A Large Multipurpose Motion and Video Dataset. arXiv:2003.01888 [cs.CV]"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1109355108"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1952.sp004764"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1095-8649.1993.tb01184.x"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/raq.12501"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3051842"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1139\/f89-097"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aquaculture.2005.10.029"},{"key":"e_1_2_2_24_1","volume-title":"The behaviour of Atlantic salmon in relation to efficient cage-rearing. Reviews in fish biology and fisheries 5, 3","author":"Juell Jon-Erik","year":"1995","unstructured":"Jon-Erik Juell . 1995. The behaviour of Atlantic salmon in relation to efficient cage-rearing. Reviews in fish biology and fisheries 5, 3 ( 1995 ), 320--335. Jon-Erik Juell. 1995. The behaviour of Atlantic salmon in relation to efficient cage-rearing. Reviews in fish biology and fisheries 5, 3 (1995), 320--335."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/0144-8609(93)90023-5"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1107583108"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10872-007-0063-0"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2005.05.019"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.2307\/2265553"},{"key":"e_1_2_2_30_1","volume-title":"function, and locomotory habits in fish. Fish Physiology VII","author":"Form CC LINDSEY.","year":"1978","unstructured":"CC LINDSEY. 1978. Form , function, and locomotory habits in fish. Fish Physiology VII ( 1978 ), 1--100. https:\/\/ci.nii.ac.jp\/naid\/10024970754\/ CC LINDSEY. 1978. Form, function, and locomotory habits in fish. Fish Physiology VII (1978), 1--100. https:\/\/ci.nii.ac.jp\/naid\/10024970754\/"},{"key":"e_1_2_2_31_1","volume-title":"Fish swimming in schools save energy regardless of their spatial position. Behavioral ecology and sociobiology 69, 2","author":"Marras Stefano","year":"2015","unstructured":"Stefano Marras , Shaun S Killen , Jan Lindstr\u00f6m , David J McKenzie , John F Steffensen , and Paolo Domenici . 2015. Fish swimming in schools save energy regardless of their spatial position. Behavioral ecology and sociobiology 69, 2 ( 2015 ), 219--226. Stefano Marras, Shaun S Killen, Jan Lindstr\u00f6m, David J McKenzie, John F Steffensen, and Paolo Domenici. 2015. Fish swimming in schools save energy regardless of their spatial position. Behavioral ecology and sociobiology 69, 2 (2015), 219--226."},{"key":"e_1_2_2_32_1","unstructured":"Ollie Matthews Koki Ryu and Tarun Srivastava. 2020. Creating a Large-scale Synthetic Dataset for Human Activity Recognition. arXiv:2007.11118 [cs.CV]  Ollie Matthews Koki Ryu and Tarun Srivastava. 2020. Creating a Large-scale Synthetic Dataset for Human Activity Recognition. arXiv:2007.11118 [cs.CV]"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.1548-16.2017"},{"key":"e_1_2_2_34_1","first-page":"151","article-title":"A theoretical approach to the attenuation coefficient of light in sea water","volume":"16","author":"Murty AVS","year":"1969","unstructured":"AVS Murty . 1969 . A theoretical approach to the attenuation coefficient of light in sea water . Indian Journal of Fisheries 16 , 1&2 (1969), 151 -- 155 . AVS Murty. 1969. A theoretical approach to the attenuation coefficient of light in sea water. Indian Journal of Fisheries 16, 1&2 (1969), 151--155.","journal-title":"Indian Journal of Fisheries"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.91.10.4288"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1467-2979.2003.00127.x"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aquaculture.2010.11.020"},{"key":"e_1_2_2_38_1","first-page":"S163","article-title":"Patterns and mechanisms of schooling behavior in fish: a review","volume":"40","author":"Pavlov DS","year":"2000","unstructured":"DS Pavlov and AO Kasumyan . 2000 . Patterns and mechanisms of schooling behavior in fish: a review . Journal of Ichthyology 40 , 2 (2000), S163 . DS Pavlov and AO Kasumyan. 2000. Patterns and mechanisms of schooling behavior in fish: a review. Journal of Ichthyology 40, 2 (2000), S163.","journal-title":"Journal of Ichthyology"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3023368.3036845"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/3141475.3141504"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1177\/0748730407311855"},{"key":"e_1_2_2_42_1","unstructured":"Craig Reynolds. 1999. Steering Behaviors For Autonomous Characters.  Craig Reynolds. 1999. Steering Behaviors For Autonomous Characters."},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/37402.37406"},{"key":"e_1_2_2_44_1","volume-title":"Behavioral thermoregulation and the \"final preferendum\" paradigm. American zoologist 19, 1","author":"Reynolds William Wallace","year":"1979","unstructured":"William Wallace Reynolds and Martha Elizabeth Casterlin . 1979. Behavioral thermoregulation and the \"final preferendum\" paradigm. American zoologist 19, 1 ( 1979 ), 211--224. William Wallace Reynolds and Martha Elizabeth Casterlin. 1979. Behavioral thermoregulation and the \"final preferendum\" paradigm. American zoologist 19, 1 (1979), 211--224."},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-71639-x"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925977"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/48.757275"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/604471.604488"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1139\/f79-132"},{"key":"e_1_2_2_50_1","unstructured":"Penny Tarling Mauricio Cantor Albert Clap\u00e9s and Sergio Escalera. 2021. Deep learning with self-supervision and uncertainty regularization to count fish in underwater images. (2021). arXiv:2104.14964 [cs.CV]  Penny Tarling Mauricio Cantor Albert Clap\u00e9s and Sergio Escalera. 2021. Deep learning with self-supervision and uncertainty regularization to count fish in underwater images. (2021). arXiv:2104.14964 [cs.CV]"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/192161.192170"},{"key":"e_1_2_2_52_1","doi-asserted-by":"crossref","unstructured":"G\u00fcl Varol Javier Romero Xavier Martin Naureen Mahmood Michael J. Black Ivan Laptev and Cordelia Schmid. 2017. Learning from Synthetic Humans. In CVPR.  G\u00fcl Varol Javier Romero Xavier Martin Naureen Mahmood Michael J. Black Ivan Laptev and Cordelia Schmid. 2017. Learning from Synthetic Humans. In CVPR.","DOI":"10.1109\/CVPR.2017.492"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-406X(64)90153-7"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1002\/cne.901780409"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478513.3480520","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3478513.3480520","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:49Z","timestamp":1750191109000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3478513.3480520"}},"subtitle":["bio-inspired fish simulation for generating synthetic datasets"],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":54,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.1145\/3478513.3480520"],"URL":"https:\/\/doi.org\/10.1145\/3478513.3480520","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"2021-12-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}