Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification
Recently, labor shortages in the farming industry have increased the demand for automation. Object tracking technology has emerged as a critical tool for monitoring livestock through automated systems. This study focuses on tracking individual cattle using object detection and tracking algorithms. D...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855392/ |
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| author | Hyeon-Seok Sim Hyun-Chong Cho |
| author_facet | Hyeon-Seok Sim Hyun-Chong Cho |
| author_sort | Hyeon-Seok Sim |
| collection | DOAJ |
| description | Recently, labor shortages in the farming industry have increased the demand for automation. Object tracking technology has emerged as a critical tool for monitoring livestock through automated systems. This study focuses on tracking individual cattle using object detection and tracking algorithms. Data were collected noninvasively using cameras, and a tracking-by-detection (TBD) approach was adopted. The proposed framework introduces multiple enhancements optimized for cattle tracking. These enhancements include a comparison of five different bounding box regression losses to improve detection accuracy, modifications to the Kalman filter state vector for more accurate bounding box predictions, and adjustments to the feature vector distance metric in the re-identification algorithm. YOLOv9-t was used as the detector, whereas DeepSORT and StrongSORT served as trackers. Compared with the baseline, which uses DeepSORT, the proposed method achieved significant improvements in higher-order tracking accuracy (HOTA) by 4.1%, multiple object tracking accuracy (MOTA) by 1.08%, and identification F1 score (IDF1) by 5.12%, reaching values of 78.64%, 90.29%, and 91.41%, respectively, while reducing the number of ID switches (IDSW). |
| format | Article |
| id | doaj-art-1e5c9a9a77b54f32b860b0c3d943daba |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-1e5c9a9a77b54f32b860b0c3d943daba2025-01-31T23:05:02ZengIEEEIEEE Access2169-35362025-01-0113193531936410.1109/ACCESS.2025.353509210855392Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-IdentificationHyeon-Seok Sim0https://orcid.org/0009-0003-9183-5205Hyun-Chong Cho1https://orcid.org/0000-0003-2122-468XDepartment Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon-si, Republic of KoreaDepartment Graduate Program for BIT Medical Convergence, Kangwon National University, Chuncheon-si, Republic of KoreaRecently, labor shortages in the farming industry have increased the demand for automation. Object tracking technology has emerged as a critical tool for monitoring livestock through automated systems. This study focuses on tracking individual cattle using object detection and tracking algorithms. Data were collected noninvasively using cameras, and a tracking-by-detection (TBD) approach was adopted. The proposed framework introduces multiple enhancements optimized for cattle tracking. These enhancements include a comparison of five different bounding box regression losses to improve detection accuracy, modifications to the Kalman filter state vector for more accurate bounding box predictions, and adjustments to the feature vector distance metric in the re-identification algorithm. YOLOv9-t was used as the detector, whereas DeepSORT and StrongSORT served as trackers. Compared with the baseline, which uses DeepSORT, the proposed method achieved significant improvements in higher-order tracking accuracy (HOTA) by 4.1%, multiple object tracking accuracy (MOTA) by 1.08%, and identification F1 score (IDF1) by 5.12%, reaching values of 78.64%, 90.29%, and 91.41%, respectively, while reducing the number of ID switches (IDSW).https://ieeexplore.ieee.org/document/10855392/Cattle trackingDeepSORTmulti-object trackingStrongSORTYOLOv9-t |
| spellingShingle | Hyeon-Seok Sim Hyun-Chong Cho Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification IEEE Access Cattle tracking DeepSORT multi-object tracking StrongSORT YOLOv9-t |
| title | Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification |
| title_full | Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification |
| title_fullStr | Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification |
| title_full_unstemmed | Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification |
| title_short | Enhanced DeepSORT and StrongSORT for Multicattle Tracking With Optimized Detection and Re-Identification |
| title_sort | enhanced deepsort and strongsort for multicattle tracking with optimized detection and re identification |
| topic | Cattle tracking DeepSORT multi-object tracking StrongSORT YOLOv9-t |
| url | https://ieeexplore.ieee.org/document/10855392/ |
| work_keys_str_mv | AT hyeonseoksim enhanceddeepsortandstrongsortformulticattletrackingwithoptimizeddetectionandreidentification AT hyunchongcho enhanceddeepsortandstrongsortformulticattletrackingwithoptimizeddetectionandreidentification |