@article{01JW8K2W93J4CV99TBGZ2DST53,
  abstract     = {{Welfare concerns in poultry farming have driven the need for advanced monitoring solutions to study broiler activity and health. However, existing research predominantly relies on single-camera setups, which are prone to occlusions from equipment such as feeders and lighting, limiting their effectiveness. To address this, we propose a multi-camera setup that enables comprehensive broiler localization and tracking from a top-down view of the pen. To support this approach, we introduce MVBroTrack,1 an open-source dataset containing realworld data with annotations for various subtasks critical to broiler studies. We demonstrate robust performance of our multi-view detection pipeline throughout the six-week broiler lifespan despite significant changes in visual appearance. Additionally, we present a novel unsupervised tracking method that surpasses the traditional tracking by detection paradigm, improving the IDF1 score by 3% and increasing the proportion of mostly tracked broilers by 5%. By reducing the need for manual observation, our multi-camera pipeline facilitates exhaustive studies of broiler behavior and welfare, paving the way for significant advancements in poultry research and farming practices.}},
  articleno    = {{110435}},
  author       = {{Cardoen, Thorsten and Soster de Carvalho, Patricia and Antonissen, Gunther and Tuyttens, Frank A. M. and Leroux, Sam and Simoens, Pieter}},
  issn         = {{0168-1699}},
  journal      = {{COMPUTERS AND ELECTRONICS IN AGRICULTURE}},
  keywords     = {{Multi-camera detection,Object tracking,Broiler welfare monitoring,Sensor fusion}},
  language     = {{eng}},
  number       = {{Part A}},
  pages        = {{17}},
  title        = {{Multi-camera detection and tracking for individual broiler monitoring}},
  url          = {{http://doi.org/10.1016/j.compag.2025.110435}},
  volume       = {{237}},
  year         = {{2025}},
}

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