{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120131"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120131","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Human-assisted high throughput livestock tracking using computer vision","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Pradeep Senthil, accepted the attached license on 2023-04-27 at 23:45.","The student, Pradeep Senthil, submitted this Thesis for approval on 2023-04-28 at 01:14.","This Thesis was approved for publication on 2023-05-01 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19227 on 2023-09-01 at 16:55:46","As Precision Livestock Farming (PLF) gains prominence in the livestock industry, the need for efficient data synthesis has grown significantly, posing new challenges. This thesis focuses on the application of novel systems using computer vision to enhance the efficiency of livestock monitoring and accelerate research in the domain. Firstly, we introduce a robust annotation system designed to minimize the time required for annotators of any skill level to process large volumes of raw video data while catering to the needs of both Animal Science and Computer Science researchers. We implement these system designs in an open-source tool called the Animal Video Annotation Tool (AVAT). Secondly, we address a critical challenge in analyzing large amounts of pig video research data. While significant progress has been made in tracking pigs across pens, most existing methods primarily focus on commercial farming applications, neglecting essential research aspects. To bridge this gap, we propose a high-throughput video analysis pipeline that tracks pig movement over several weeks, offering insights into the movement patterns over time. These insights facilitate understanding behavioral changes resulting from varying stimuli and treatment plans, leading to the development of improved nutritional strategies and practices that promote pig health and welfare. To evaluate our pipeline, we analyzed 13TB of video data collected from a live animal study, allowing us to observe movement trends across time and empowering animal science researchers to draw conclusions about pig behavior in response to their treatment plans."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Human-assisted high throughput livestock tracking using computer vision"]}]}],"canonical_facts":{"dc:contributor":["Caesar, Matthew","Dilger, Ryan N"],"dc:creator":["Senthil, Pradeep"],"dc:date":["2023-05","2023-05-01"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Pradeep Senthil, accepted the attached license on 2023-04-27 at 23:45.","The student, Pradeep Senthil, submitted this Thesis for approval on 2023-04-28 at 01:14.","This Thesis was approved for publication on 2023-05-01 at 16:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19227 on 2023-09-01 at 16:55:46","As Precision Livestock Farming (PLF) gains prominence in the livestock industry, the need for efficient data synthesis has grown significantly, posing new challenges. This thesis focuses on the application of novel systems using computer vision to enhance the efficiency of livestock monitoring and accelerate research in the domain. 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These insights facilitate understanding behavioral changes resulting from varying stimuli and treatment plans, leading to the development of improved nutritional strategies and practices that promote pig health and welfare. To evaluate our pipeline, we analyzed 13TB of video data collected from a live animal study, allowing us to observe movement trends across time and empowering animal science researchers to draw conclusions about pig behavior in response to their treatment plans."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120131"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Pradeep Senthil"],"dc:subject":["Pig Monitoring","Livestock","Precision Livestock Farming","Object Detection","Computer Vision","Annotation System","Data Management"],"dc:title":["Human-assisted high throughput livestock tracking using computer vision"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}