GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity
Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we prop...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/15/7/1040 |
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| author | Zhaoyang Yin Zehua Wang Junhua Ye Suyin Zhou Aijun Xu |
| author_facet | Zhaoyang Yin Zehua Wang Junhua Ye Suyin Zhou Aijun Xu |
| author_sort | Zhaoyang Yin |
| collection | DOAJ |
| description | Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we propose a pig-tracking method that is based on skeleton feature similarity, which we named GcnTrack. We used YOLOv7-Pose to extract pig skeleton key points and design a dual-tracking strategy. This strategy combines IOU matching and skeleton keypoint-based graph convolutional reidentification (Re-ID) algorithms to track pigs continuously, even when pigs return from outside the FOV. Three identical FOV sets of data that separately included long, medium, and short duration videos were used to test the model and verify its performance. The GcnTrack method achieved a Multiple Object Tracking Accuracy (MOTA) of 84.98% and an identification F1 Score (IDF1) of 82.22% for the first set of videos (short duration, 87 s to 220 s). The tracking precision was 74% for the second set of videos (medium duration, average 302 s). The pigs entered the scene 15.29 times on average, with an average of 6.28 identity switches (IDSs) per pig during the tracking experiments on the third batch set of videos (long duration, 14 min). In conclusion, our method contributes an accurate and reliable pig-tracking solution applied to scenarios with incomplete camera FOV. |
| format | Article |
| id | doaj-art-198600c2a1984a17bd5bed84caa1d65c |
| institution | DOAJ |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-198600c2a1984a17bd5bed84caa1d65c2025-08-20T03:08:43ZengMDPI AGAnimals2076-26152025-04-01157104010.3390/ani15071040GCNTrack: A Pig-Tracking Method Based on Skeleton Feature SimilarityZhaoyang Yin0Zehua Wang1Junhua Ye2Suyin Zhou3Aijun Xu4School of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaSchool of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaSchool of Environmental and Resource Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaSchool of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaSchool of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, ChinaPig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we propose a pig-tracking method that is based on skeleton feature similarity, which we named GcnTrack. We used YOLOv7-Pose to extract pig skeleton key points and design a dual-tracking strategy. This strategy combines IOU matching and skeleton keypoint-based graph convolutional reidentification (Re-ID) algorithms to track pigs continuously, even when pigs return from outside the FOV. Three identical FOV sets of data that separately included long, medium, and short duration videos were used to test the model and verify its performance. The GcnTrack method achieved a Multiple Object Tracking Accuracy (MOTA) of 84.98% and an identification F1 Score (IDF1) of 82.22% for the first set of videos (short duration, 87 s to 220 s). The tracking precision was 74% for the second set of videos (medium duration, average 302 s). The pigs entered the scene 15.29 times on average, with an average of 6.28 identity switches (IDSs) per pig during the tracking experiments on the third batch set of videos (long duration, 14 min). In conclusion, our method contributes an accurate and reliable pig-tracking solution applied to scenarios with incomplete camera FOV.https://www.mdpi.com/2076-2615/15/7/1040pigtrackingre-identificationgraph convolutional networkskeleton |
| spellingShingle | Zhaoyang Yin Zehua Wang Junhua Ye Suyin Zhou Aijun Xu GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity Animals pig tracking re-identification graph convolutional network skeleton |
| title | GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity |
| title_full | GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity |
| title_fullStr | GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity |
| title_full_unstemmed | GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity |
| title_short | GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity |
| title_sort | gcntrack a pig tracking method based on skeleton feature similarity |
| topic | pig tracking re-identification graph convolutional network skeleton |
| url | https://www.mdpi.com/2076-2615/15/7/1040 |
| work_keys_str_mv | AT zhaoyangyin gcntrackapigtrackingmethodbasedonskeletonfeaturesimilarity AT zehuawang gcntrackapigtrackingmethodbasedonskeletonfeaturesimilarity AT junhuaye gcntrackapigtrackingmethodbasedonskeletonfeaturesimilarity AT suyinzhou gcntrackapigtrackingmethodbasedonskeletonfeaturesimilarity AT aijunxu gcntrackapigtrackingmethodbasedonskeletonfeaturesimilarity |