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A Stable Long-Term Tracking Method for Group-Housed Pigs

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14356))

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Abstract

In recent years, computer vision technologies have been increasingly applied to livestock farming for improving efficiency and reducing the labor force in surveillance. Tracking of group-housed pigs is an important task for monitoring the daily behaviors of pigs, which can be used to preliminarily evaluate the health status of pigs. Most researchers directly apply existing multi-object tracking algorithms to this task, but often suffer from tracking failures due to false detection, stacking, occlusion, video jamming, etc. It usually produces a lot of incorrect ID switches that are disastrous for follow-up tasks. In this paper, we propose a group-housed pigs tracking method that can achieve stable long-term tracking. As the identity and number of monitored pigs remain unchanged during a feeding period, we introduce a new object matching mechanism with a classifier, which avoids most incorrect ID switches and effectively improves the matching accuracy. Thus, our tracking method is more robust to complex posture variations of the pig and achieves stable long-term tracking. The experimental results on real videos captured in a pigs farm prove the effectiveness of our method.

Supported by “Scientific and Technological Innovation 2030” Program of China Ministry of Science and Technology (2021ZD0113803).

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Author information

Authors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Shibo Gao & Linlin Huang

  2. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shibo Gao & Peipei Yang

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Peipei Yang

  4. College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China

    Jinmeng Gong & Chao Liang

  5. Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, Beijing, China

    Chao Liang

Authors
  1. Shibo Gao
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  2. Jinmeng Gong
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  3. Peipei Yang
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  4. Chao Liang
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  5. Linlin Huang
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Corresponding author

Correspondence to Peipei Yang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. University of Sydney, Sydney, NSW, Australia

    Wanli Ouyang

  3. Shenzhen University, Shenzhen, China

    Hui Huang

  4. Tsinghua University, Beijing, China

    Jiwen Lu

  5. Dalian University of Technology, Dalian, China

    Risheng Liu

  6. Institute of Automation, CAS, Beijing, China

    Jing Dong

  7. University of Technology Sydney, Sydney, NSW, Australia

    Min Xu

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Gao, S., Gong, J., Yang, P., Liang, C., Huang, L. (2023). A Stable Long-Term Tracking Method for Group-Housed Pigs. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14356. Springer, Cham. https://doi.org/10.1007/978-3-031-46308-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-46308-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46307-5

  • Online ISBN: 978-3-031-46308-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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