Group-housed pigs tracking based on the attention mechanism
Article No.: 51, Pages 1 - 8
Abstract
The estimation of motion behavior of group-housed pigs can be used to predict their health condition statistically. A feasible motion detection method is to use contact sensors, such as adding wireless inertial sensors to pigs, which will cause stress responses in pigs and bring high costs. Different from traditional methods, video Multi-Object Tracking (MOT) technology studies the daily movement of pigs indirectly via deep learning methods. However, MOT for group-housed pigs faces many research difficulties, such as deformation, adhesion, occlusion, appearance similarity, light disturbance and background interference. In order to increase the robustness of the tracking algorithm, an improved FairMOT algorithm based on attention mechanism is proposed, in which Convolutional Block Attention Modules (CBAM) are integrated into the DLA34 backbone as dlaCBAM and Multi-Object Tracking Accuracy (MOTA) rose to 90.7%. The data association applies Kalman filter and Hungarian algorithm, and euclidean distance is adopted to characterize the amount of movement and construct the daily trajectory of pigs. Test results show that the improved FairMOT algorithm has a better tracking effect on pigs, which provides new ideas for intelligent pig motion estimation.
References
[1]
Maselyne, J., Adriaens, I., Huybrechts, T., De Ketelaere, B., Millet, S., Vangeyte, J., Van Nuffel, A., and Saeys, W., “Measuring the drinking behaviour of individual pigs housed in group using radio frequency identification (rfid),” Animal 10(9), 1557–1566 (2016).
[2]
Kashiha, M., Bahr, C., Ott, S., Moons, C. P., Niewold, T. A., ̈Odberg, F. O., and Berckmans, D., “Automatic identification of marked pigs in a pen using image pattern recognition,” Computers and electronics in agriculture 93, 111–120 (2013).
[3]
Neethirajan, S., “Happy cow or thinking pig? wur wolf–facial coding platform for measuring emotions in farm animals,” bioRxiv (2021).
[4]
Mellor, D. J., “Updating animal welfare thinking: Moving beyond the “five freedoms” towards “a life worth living”,” Animals 6(3), 21 (2016).
[5]
Zhang, L., Gray, H., Ye, X., Collins, L., and Allinson, N., “Automatic individual pig detection and tracking in pig farms,” Sensors 19(5), 1188 (2019).
[6]
T Psota, E., Schmidt, T., Mote, B., and C P ́erez, L., “Long-term tracking of group-housed livestock using keypoint detection and map estimation for individual animal identification,” Sensors 20(13), 3670 (2020).
[7]
Chen, G., Shen, S., Wen, L., Luo, S., and Bo, L., “Efficient pig counting in crowds with keypoints tracking and spatial-aware temporal response filtering,” in [2020 IEEE International Conference on Robotics and Automation (ICRA)], 10052–10058, IEEE (2020).
[8]
Chen, F., Liang, X., Chen, L., Liu, B., and Lan, Y., “Novel method for real-time detection and tracking of pig body and its different parts,” International Journal of Agricultural and Biological Engineering 13(6), 144–149 (2020).
[9]
Bolya, D., Zhou, C., Xiao, F., and Lee, Y. J., “Yolact: Real-time instance segmentation,” in [Proceedings of the IEEE/CVF International Conference on Computer Vision], 9157–9166 (2019).
[10]
Casagrande, J. H. B. and Stemmer, M. R., “Abnormal motion analysis for tracking-based approaches using region-based method with mobile grid,” Journal of Image and Graphics 2(1), 22–27 (2014).
[11]
Van Der Zande, L., Guzhva, O., Rodenburg, T. B., “Individual detection and tracking of group housed pigs in their home pen using computer vision,” Frontiers in Animal Science 2, 10 (2021).
[12]
Redmon, J. and Farhadi, A., “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767 (2018).
[13]
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B., “Simple online and realtime tracking,” in [2016 IEEE international conference on image processing (ICIP)], 3464–3468, IEEE (2016)
[14]
Zhang, Y., Wang, C., Wang, X., Zeng, W., and Liu, W., “Fairmot: On the fairness of detection and reidentification in multiple object tracking,” International Journal of Computer Vision 129(11), 3069–3087 (2021).
[15]
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C., “Ssd: Single shot multibox detector,” in [European conference on computer vision], 21–37, Springer (2016).
[16]
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A., “You only look once: Unified, real-time object detection,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 779–788 (2016).
[17]
Yu, F., Wang, D., Shelhamer, E., and Darrell, T., “Deep layer aggregation,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 2403–2412 (2018).
[18]
Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S., “Cbam: Convolutional block attention module,” in [Proceedings of the European conference on computer vision (ECCV)], 3–19 (2018).
[19]
Milan, A., Leal-Taix ́e, L., Reid, I., Roth, S., and Schindler, K., “Mot16: A benchmark for multi-object tracking,” arXiv preprint arXiv:1603.00831 (2016).
[20]
Sun, K., Xiao, B., Liu, D., and Wang, J., “Deep high-resolution representation learning for human pose estimation,” in [Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition], 5693–5703 (2019).
[21]
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., “Searching for mobilenetv3,” in [Proceedings of the IEEE/CVF International Conference on Computer Vision], 1314–1324 (2019).
[22]
Lin, T.-Y., Doll ́ar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S., “Feature pyramid networks for object detection,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 2117–2125 (2017).
[23]
Zhu, X., Hu, H., Lin, S., and Dai, J., “Deformable convnets v2: More deformable, better results,” in [Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition], 9308–9316 (2019).
[24]
Kendall, A., Gal, Y., and Cipolla, R., “Multi-task learning using uncertainty to weigh losses for scene geometry and semantics,” in [Proceedings of the IEEE conference on computer vision and pattern recognition], 7482–7491 (2018).
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- Group-housed pigs tracking based on the attention mechanism
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Published In
May 2023
711 pages
ISBN:9798400708237
DOI:10.1145/3604078
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 26 October 2023
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ICDIP 2023
ICDIP 2023: The 15th International Conference on Digital Image Processing
May 19 - 22, 2023
Nanjing, China
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