RpTrack: Robust Pig Tracking with Irregular Movement Processing and Behavioral Statistics

Shuqin Tu, Hua Lei, Yun Liang (), Enli Lyu and Hongxing Liu
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Shuqin Tu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Hua Lei: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yun Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Enli Lyu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Hongxing Liu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 7, 1-16

Abstract: Pig behavioral analysis based on multi-object tracking (MOT) technology of surveillance videos is vital for precision livestock farming. To address the challenges posed by uneven lighting scenes and irregular pig movements in the MOT task, we proposed a pig MOT method named RpTrack. Firstly, RpTrack addresses the issue of lost tracking caused by irregular pig movements by using an appropriate Kalman Filter and improved trajectory management. Then, RpTrack utilizes BIoU for the second matching strategy to alleviate the influence of missed detections on the tracking performance. Finally, the method utilizes post-processing on the tracking results to generate behavioral statistics and activity trajectories for each pig. The experimental results under conditions of uneven lighting and irregular pig movements show that RpTrack significantly outperforms four other state-of-the-art MOT methods, including SORT, OC-SORT, ByteTrack, and Bot-SORT, on both public and private datasets. The experimental results demonstrate that RpTrack not only has the best tracking performance but also has high-speed processing capabilities. In conclusion, RpTrack effectively addresses the challenges of uneven scene lighting and irregular pig movements, enabling accurate pig tracking and monitoring of different behaviors, such as eating, standing, and lying. This research supports the advancement and application of intelligent pig farming.

Keywords: multi-object tracking; pigs; RpTrack; behavioral statistics; uneven lighting scenes; irregular pig movements (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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