Research on swine trajectory tracking algorithm based on object detection

Dan Li; Shuo Xu; Feng Lu; Yifei Chen; Muhan Xue; Guohui Cui

doi:10.1117/12.3017909

29 January 2024 Research on swine trajectory tracking algorithm based on object detection

Dan Li, Shuo Xu, Feng Lu, Yifei Chen, Muhan Xue, Guohui Cui

Proceedings Volume 12984, Fourth International Conference on Computer Vision and Information Technology (CVIT 2023); 1298406 (2024) https://doi.org/10.1117/12.3017909
Event: 2023 4th International Conference on Computer Vision and Information Technology (CVIT 2023), 2023, Beijing, China

Abstract

The tracking algorithm for swine plays a pivotal role in efficiently extracting the movement trajectories and quantifying the motion patterns of pigs, thereby serving as an indicator of their physical well-being. Consequently, the task of swine tracking assumes paramount significance. Addressing the issues of low automation, significant error rates, and poor real-time performance in pig tracking, this study introduced a deep learning-based algorithm for swine tracking. It encompasses the development of a pig target detection model based on RetinaNet and introduces an innovative strategy for swine trajectory tracking incorporating time-series information, facilitating real-time tracking of multiple pig targets. The results from algorithm testing demonstrated the effectiveness of the swine target detection algorithm based on RetinaNet, with an AP₅₀ of 0.998, AP₇₅ of 0.907, AP₉₀ of 0.606, and an operational speed of 42.3 tasks per second. This underscored the algorithm's capacity to proficiently detect pig target categories and delineate precise target bounding boxes. In terms of swine target detection, the multi-object trajectory tracking algorithm achieved an average Multi-Object Tracking Precision of 2.37 pixels, equivalent to approximately 1.83 cm in distance. Furthermore, it attained an average Multi-Object Tracking Accuracy of 97.44%, thus substantiating its aptitude for the effective tracking of multiple pig targets with an exceptional level of tracking precision and consistency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dan Li, Shuo Xu, Feng Lu, Yifei Chen, Muhan Xue, and Guohui Cui "Research on swine trajectory tracking algorithm based on object detection", Proc. SPIE 12984, Fourth International Conference on Computer Vision and Information Technology (CVIT 2023), 1298406 (29 January 2024); https://doi.org/10.1117/12.3017909

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