A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism
Summary: Given that the pig behavior reflects their health status, continuous and precise monitoring of behavior is important for effective health management and welfare protection. To mitigate potential tracking failures during analysis of video footage, we introduced a novel multi-target pig track...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Agriculture Communications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949798124000383 |
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| author | Qifeng Li Zhenyuan Zhuo Ronghua Gao Rong Wang Na Zhang Yan Shi Tonghui Wu Weihong Ma |
| author_facet | Qifeng Li Zhenyuan Zhuo Ronghua Gao Rong Wang Na Zhang Yan Shi Tonghui Wu Weihong Ma |
| author_sort | Qifeng Li |
| collection | DOAJ |
| description | Summary: Given that the pig behavior reflects their health status, continuous and precise monitoring of behavior is important for effective health management and welfare protection. To mitigate potential tracking failures during analysis of video footage, we introduced a novel multi-target pig tracking method that consisted of detection and tracking components. The detection model was enhanced with an efficient attention mechanism and a Cross Stage Partial Darknet backbone network, which significantly improved detection accuracy. The tracking component used the Bytetrack algorithm to accurately track the movement trajectories of individual pigs. Together, these components were combined into the Dual-YOLOX-Tiny-ByteTrack (DYTB) model, which demonstrated superior performance in automatic monitoring of pig behaviors compared to previously published approaches. We established multi-object pig tracking datasets with 180,321 images to evaluate this method. The DYTB method achieved a pig detection accuracy of 98.3% and tracking accuracies of 95.3% and 97.1%. Compared to the YOLOX-Tiny-ByteTrack base model, DYTB showed a 3.4% improvement in multiple object tracking accuracy, making it a robust method for non-contact, intelligent monitoring of pig health and contributing to advances in precision livestock farming. |
| format | Article |
| id | doaj-art-d7ccbcc3b0064c8e851e6389f7d2dfc6 |
| institution | Kabale University |
| issn | 2949-7981 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agriculture Communications |
| spelling | doaj-art-d7ccbcc3b0064c8e851e6389f7d2dfc62024-12-25T04:21:43ZengElsevierAgriculture Communications2949-79812024-12-0124100062A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanismQifeng Li0Zhenyuan Zhuo1Ronghua Gao2Rong Wang3Na Zhang4Yan Shi5Tonghui Wu6Weihong Ma7Information Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing University of Agriculture, Beijing 102206, ChinaInformation Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing University of Agriculture, Beijing 102206, ChinaInformation Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing University of Agriculture, Beijing 102206, China; Corresponding author. Information Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.Information Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaBeijing University of Agriculture, Beijing 102206, ChinaInformation Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Northwest Agriculture and Forestry University, Yangling, 712100, ChinaInformation Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Shanxi Agricultural University, Jinzhong, 030801, ChinaInformation Technology Research Centre, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Beijing University of Agriculture, Beijing 102206, ChinaSummary: Given that the pig behavior reflects their health status, continuous and precise monitoring of behavior is important for effective health management and welfare protection. To mitigate potential tracking failures during analysis of video footage, we introduced a novel multi-target pig tracking method that consisted of detection and tracking components. The detection model was enhanced with an efficient attention mechanism and a Cross Stage Partial Darknet backbone network, which significantly improved detection accuracy. The tracking component used the Bytetrack algorithm to accurately track the movement trajectories of individual pigs. Together, these components were combined into the Dual-YOLOX-Tiny-ByteTrack (DYTB) model, which demonstrated superior performance in automatic monitoring of pig behaviors compared to previously published approaches. We established multi-object pig tracking datasets with 180,321 images to evaluate this method. The DYTB method achieved a pig detection accuracy of 98.3% and tracking accuracies of 95.3% and 97.1%. Compared to the YOLOX-Tiny-ByteTrack base model, DYTB showed a 3.4% improvement in multiple object tracking accuracy, making it a robust method for non-contact, intelligent monitoring of pig health and contributing to advances in precision livestock farming.http://www.sciencedirect.com/science/article/pii/S2949798124000383Multi-objectBehavioural trackingTarget detectionByteTrackEfficient attention mechanism |
| spellingShingle | Qifeng Li Zhenyuan Zhuo Ronghua Gao Rong Wang Na Zhang Yan Shi Tonghui Wu Weihong Ma A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism Agriculture Communications Multi-object Behavioural tracking Target detection ByteTrack Efficient attention mechanism |
| title | A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism |
| title_full | A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism |
| title_fullStr | A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism |
| title_full_unstemmed | A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism |
| title_short | A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism |
| title_sort | pig behavior tracking method based on a multi channel high efficiency attention mechanism |
| topic | Multi-object Behavioural tracking Target detection ByteTrack Efficient attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S2949798124000383 |
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