Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method
The management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. Due to rapid pig movement and frequent occlusions during aggression activities, the key challenges for pig tracking and aggre...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525005428 |
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| author | Shuqin Tu Haoxuan Ou Aqing Yang Yun Liang Jiaying Du Yuefei Cao Ruilin He Fang Yuan |
| author_facet | Shuqin Tu Haoxuan Ou Aqing Yang Yun Liang Jiaying Du Yuefei Cao Ruilin He Fang Yuan |
| author_sort | Shuqin Tu |
| collection | DOAJ |
| description | The management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. Due to rapid pig movement and frequent occlusions during aggression activities, the key challenges for pig tracking and aggression analysis using multi-object tracking (MOT) are low tracking accuracy and error identity-switching (IDs) problems. To address the above challenges, this study proposes an RPMeMOTR approach by combining the RKNet model and the P-ids algorithm based on MeMOTR for pig aggression tracking and analysis. Firstly, we create a new backbone structure named RKNet to enhance aggression feature extraction and representation learning ability. Then, we design the P-ids algorithm to ensure stable tracking by preserving and leveraging historical trajectory data. Finally, we develop an analysis algorithm to quantify the intensity of pig aggression to enable alerts for farm staff. In public database, RPMeMOTR achieved an average Higher Order Tracking Accuracy (HOTA) of 78.2%, Multi-Object Tracking Accuracy (MOTA) of 93.8%, identification F1 Score (IDF1) of 94.9%, and IDs of 7. Compared to MeMOTR, it demonstrated significant improvements in HOTA, MOTA, IDF1, and IDs metrics with 5.0%, 6.6%, 7.0% increase, and 11 reduces. And using the RPMeMOTR method, compared to OC-SORT, there was a 4.1% increase in HOTA, a 7.4% increase in IDF1 and 28 reduces in IDs, while compared to ByteTrack, its improvements were 4.8% in HOTA,8.6% in IDF1 and 33 reduces in IDs. It also shows superiority over other methods in private datasets. These experimental results demonstrated that the proposed approach offered an effective solution for the accurate tracking and aggression behavior analysis of pigs in intricate farm environments, meeting the requirements for long-time monitoring and intervention. |
| format | Article |
| id | doaj-art-739c3fd0ebba48e5afb93a73e08fcae2 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-739c3fd0ebba48e5afb93a73e08fcae22025-08-20T05:08:11ZengElsevierSmart Agricultural Technology2772-37552025-12-011210131110.1016/j.atech.2025.101311Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR MethodShuqin Tu0Haoxuan Ou1Aqing Yang2Yun Liang3Jiaying Du4Yuefei Cao5Ruilin He6Fang Yuan7College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China; Corresponding author.College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, ChinaThe management of pig aggression in group-housed environments is crucial for ensuring animal welfare and optimizing production efficiency in the global agricultural industry. Due to rapid pig movement and frequent occlusions during aggression activities, the key challenges for pig tracking and aggression analysis using multi-object tracking (MOT) are low tracking accuracy and error identity-switching (IDs) problems. To address the above challenges, this study proposes an RPMeMOTR approach by combining the RKNet model and the P-ids algorithm based on MeMOTR for pig aggression tracking and analysis. Firstly, we create a new backbone structure named RKNet to enhance aggression feature extraction and representation learning ability. Then, we design the P-ids algorithm to ensure stable tracking by preserving and leveraging historical trajectory data. Finally, we develop an analysis algorithm to quantify the intensity of pig aggression to enable alerts for farm staff. In public database, RPMeMOTR achieved an average Higher Order Tracking Accuracy (HOTA) of 78.2%, Multi-Object Tracking Accuracy (MOTA) of 93.8%, identification F1 Score (IDF1) of 94.9%, and IDs of 7. Compared to MeMOTR, it demonstrated significant improvements in HOTA, MOTA, IDF1, and IDs metrics with 5.0%, 6.6%, 7.0% increase, and 11 reduces. And using the RPMeMOTR method, compared to OC-SORT, there was a 4.1% increase in HOTA, a 7.4% increase in IDF1 and 28 reduces in IDs, while compared to ByteTrack, its improvements were 4.8% in HOTA,8.6% in IDF1 and 33 reduces in IDs. It also shows superiority over other methods in private datasets. These experimental results demonstrated that the proposed approach offered an effective solution for the accurate tracking and aggression behavior analysis of pigs in intricate farm environments, meeting the requirements for long-time monitoring and intervention.http://www.sciencedirect.com/science/article/pii/S2772375525005428MOTMeMOTRRKNet modelP-idsRPMeMOTRPig aggression Analysis |
| spellingShingle | Shuqin Tu Haoxuan Ou Aqing Yang Yun Liang Jiaying Du Yuefei Cao Ruilin He Fang Yuan Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method Smart Agricultural Technology MOT MeMOTR RKNet model P-ids RPMeMOTR Pig aggression Analysis |
| title | Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method |
| title_full | Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method |
| title_fullStr | Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method |
| title_full_unstemmed | Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method |
| title_short | Pig Aggression Tracking and Analysis Application Based on A RPMeMOTR Method |
| title_sort | pig aggression tracking and analysis application based on a rpmemotr method |
| topic | MOT MeMOTR RKNet model P-ids RPMeMOTR Pig aggression Analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525005428 |
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