{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110553"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110553","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"High throughput livestock monitoring using computer vision","abstract":"Increasing demand for meat products combined with farm labor shortages has resulted in a need to develop new real-time solutions to monitor animals effectively. Extracting valuable information about pigs from video data is a challenging problem. The focus of this thesis is to apply computer vision techniques on animal video feeds to ensure high throughput monitoring of livestock. We first consider a pig dataset acquired in controlled lab environments and apply state-of-the-art action recognition methods to it. The 'Pig Novelty Preference Behavioral Dataset' is used to train and validate the performance of different models. We obtain an accuracy of more than 93% and a precision of more than 90% for predicting pig behavior. We open-source our code and annotated dataset at https://github.com/AIFARMS/NOR-behavior-recognition We then consider a pig dataset acquired in farm environments. Significant progress has been made in continuously locating individual pigs using tracking-by-detection methods. However, these methods fail for oblong pens because a single fixed camera does not cover the entire floor at adequate resolution. We address this problem by using multiple cameras, placed such that the visual fields of adjacent cameras overlap, and together they span the entire floor. Avoiding breaks in tracking requires inter-camera handover when a pig crosses from one camera’s view into that of an adjacent camera. We identify the adjacent camera and the shared pig location on the floor at the handover time using inter-view homography. Additionally, we estimate the time spent at the feeder and drinker for each pig based on proximity to the feeder or drinker. For evaluating the detection module, we use the usual average height (precision) of the precision-recall curve; we obtain 99% for Intersection over Union threshold of 50%. We open-source our code and annotated dataset at https://github.com/AIFARMS/multi-camera-pig-tracking","abstract_html":"Increasing demand for meat products combined with farm labor shortages has resulted in a need to develop new real-time solutions to monitor animals effectively. Extracting valuable information about pigs from video data is a challenging problem. The focus of this thesis is to apply computer vision techniques on animal video feeds to ensure high throughput monitoring of livestock. We first consider a pig dataset acquired in controlled lab environments and apply state-of-the-art action recognition methods to it. The 'Pig Novelty Preference Behavioral Dataset' is used to train and validate the performance of different models. We obtain an accuracy of more than 93% and a precision of more than 90% for predicting pig behavior. We open-source our code and annotated dataset at https://github.com/AIFARMS/NOR-behavior-recognition We then consider a pig dataset acquired in farm environments. Significant progress has been made in continuously locating individual pigs using tracking-by-detection methods. However, these methods fail for oblong pens because a single fixed camera does not cover the entire floor at adequate resolution. We address this problem by using multiple cameras, placed such that the visual fields of adjacent cameras overlap, and together they span the entire floor. Avoiding breaks in tracking requires inter-camera handover when a pig crosses from one camera’s view into that of an adjacent camera. We identify the adjacent camera and the shared pig location on the floor at the handover time using inter-view homography. Additionally, we estimate the time spent at the feeder and drinker for each pig based on proximity to the feeder or drinker. For evaluating the detection module, we use the usual average height (precision) of the precision-recall curve; we obtain 99% for Intersection over Union threshold of 50%. We open-source our code and annotated dataset at https://github.com/AIFARMS/multi-camera-pig-tracking","abstract_has_math":false,"creators":["Shirke, Aniket"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Caesar, Matthew C"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:11:15Z","date_published":"2021-09-17T01:11:15Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Multi-Camera Tracking","Computer Vision","Pig Monitoring","Surveillance","Livestock","Action Recognition","Object Detection","Object Tracking","Homography"],"languages":["en"],"rights":["Copyright 2021 Aniket Shirke"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110553","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Caesar, Matthew C"]},{"key":"dc:creator","label":"Author","values":["Shirke, Aniket"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:11:15Z","2021-04-27","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-Camera Tracking","Computer Vision","Pig Monitoring","Surveillance","Livestock","Action Recognition","Object Detection","Object Tracking","Homography"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Aniket Shirke"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110553"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Increasing demand for meat products combined with farm labor shortages has resulted in a need to develop new real-time solutions to monitor animals effectively. Extracting valuable information about pigs from video data is a challenging problem. The focus of this thesis is to apply computer vision techniques on animal video feeds to ensure high throughput monitoring of livestock. We first consider a pig dataset acquired in controlled lab environments and apply state-of-the-art action recognition methods to it. The 'Pig Novelty Preference Behavioral Dataset' is used to train and validate the performance of different models. We obtain an accuracy of more than 93% and a precision of more than 90% for predicting pig behavior. We open-source our code and annotated dataset at https://github.com/AIFARMS/NOR-behavior-recognition We then consider a pig dataset acquired in farm environments. Significant progress has been made in continuously locating individual pigs using tracking-by-detection methods. However, these methods fail for oblong pens because a single fixed camera does not cover the entire floor at adequate resolution. We address this problem by using multiple cameras, placed such that the visual fields of adjacent cameras overlap, and together they span the entire floor. Avoiding breaks in tracking requires inter-camera handover when a pig crosses from one camera’s view into that of an adjacent camera. We identify the adjacent camera and the shared pig location on the floor at the handover time using inter-view homography. Additionally, we estimate the time spent at the feeder and drinker for each pig based on proximity to the feeder or drinker. For evaluating the detection module, we use the usual average height (precision) of the precision-recall curve; we obtain 99% for Intersection over Union threshold of 50%. We open-source our code and annotated dataset at https://github.com/AIFARMS/multi-camera-pig-tracking","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Aniket Shirke, accepted the attached license on 2021-04-22 at 16:06.","The student, Aniket Shirke, submitted this Thesis for approval on 2021-04-22 at 16:29.","This Thesis was approved for publication on 2021-04-27 at 10:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16512 on 2021-09-16 at 16:47:18","Made available in DSpace on 2021-09-17T01:11:15Z (GMT). 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We first consider a pig dataset acquired in controlled lab environments and apply state-of-the-art action recognition methods to it. The 'Pig Novelty Preference Behavioral Dataset' is used to train and validate the performance of different models. We obtain an accuracy of more than 93% and a precision of more than 90% for predicting pig behavior. We open-source our code and annotated dataset at https://github.com/AIFARMS/NOR-behavior-recognition We then consider a pig dataset acquired in farm environments. Significant progress has been made in continuously locating individual pigs using tracking-by-detection methods. However, these methods fail for oblong pens because a single fixed camera does not cover the entire floor at adequate resolution. We address this problem by using multiple cameras, placed such that the visual fields of adjacent cameras overlap, and together they span the entire floor. Avoiding breaks in tracking requires inter-camera handover when a pig crosses from one camera’s view into that of an adjacent camera. We identify the adjacent camera and the shared pig location on the floor at the handover time using inter-view homography. Additionally, we estimate the time spent at the feeder and drinker for each pig based on proximity to the feeder or drinker. For evaluating the detection module, we use the usual average height (precision) of the precision-recall curve; we obtain 99% for Intersection over Union threshold of 50%. We open-source our code and annotated dataset at https://github.com/AIFARMS/multi-camera-pig-tracking","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Aniket Shirke, accepted the attached license on 2021-04-22 at 16:06.","The student, Aniket Shirke, submitted this Thesis for approval on 2021-04-22 at 16:29.","This Thesis was approved for publication on 2021-04-27 at 10:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16512 on 2021-09-16 at 16:47:18","Made available in DSpace on 2021-09-17T01:11:15Z (GMT). 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