Multi-target pig tracking algorithm based on joint probability data association and particle filter
Keywords:
joint probability data association, pig tracking, particle filter, centroidAbstract
In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss. Keywords: joint probability data association, pig tracking, particle filter, centroid DOI: 10.25165/j.ijabe.20211404.6105 Citation: Sun L Q, Li Y Y. Multi-target pig tracking algorithm based on joint probability data association and particle filter. Int J Agric & Biol Eng, 2021; 14(4): 199–207.References
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[23] Li Y Y, Sun L Q, Zou Y B, Li Y. Individual pig object detection algorithm based on Gaussian mixture model. Int J Agric & Biol Eng, 2017; 10(5): 186–193
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[2] Moody F H, Wilkerson J B, Hart W E, Goodwin J E, Funk P A. Non-intrusive flow rate sensor for harvester and gin applications. In: Proc. Beltwide Cotton Conf., Natl. Cotton Counc. Am., Memphis, TN. 2000; pp.410–415.
[3] Xiong J T, He Z L, Lin R, Liu Z, Bu R B, Yang Z G, et al. Visual positioning technology of picking robots for dynamic litchi clusters with disturbance. Computers & Electronics in Agriculture, 2018; 151: 226–237.
[4] Hiremath S A, van der Heijden G W A M, van Evert F K, Stein A, ter Braak C J F. Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers & Electronics in Agriculture, 2014; 100: 41–50.
[5] Shu G, Dehghan A, Orifej O, Hand E, Shah M. Part-based multiple-person tracking with partial occlusion handling. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI: IEEE, 2012; pp.1815–1821. doi: 10.1109/CVPR.2012.6247879.
[6] Milan A, Roth S, Schindler K. Continuous energy minimization for multi-target tracking. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014; 36(1): 58–72.
[7] Zhang L, Li Y, Nevatia R. Global data association for multi-object tracking using network flows. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2008; pp.1–8. doi: 10.1109/ CVPR.2008.4587584.
[8] Yang Y, Li D. Robust player detection and tracking in broadcast soccer video based on enhanced particle filter. Journal of Visual Communication & Image Representation, 2017; 46: 81–94.
[9] Ma X B, Sun S F, Qin Y S, Hu S. Adaptive fusion color and Haar-like feature object tracking based on particle filter. Japanese Journal of Applied Physics, 2013; 45(1): 3686–3689.
[10] Ma L, Hu W M. Adaptive tracking with patches and a new particle filter. In: The First Asian Conference on Pattern Recognition. Beijing: IEEE, 2012; pp.387–391. doi: 10.1109/ACPR.2011.6166653.
[11] Zuriarrain I, Mekonnen A A, Lerasle F, Arana N. Tracking-by-detection of multiple persons by a resample-move particle filter. Machine Vision and Applications, 2013; 24(8): 1751–1765.
[12] Chavali P, Nehorai A. Hierarchical particle filtering for multi-modal data fusion with application to multiple–target tracking. Signal Processing, 2014; 97(7): 207–220.
[13] Butt A A, Collins R T. Multi-target tracking by Lagrangian relaxation to min-cost network flow. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR: IEEE, 2013; pp.1846–1853. doi: 10.1109/CVPR.2013.241.
[14] Chen X, Li Y A, Li Y X, Yu J, Li X H. A novel probabilistic data association for target tracking in a cluttered environment. Sensors, 2016; 16(12): 2180. doi: 10.3390/s16122180.
[15] Yi Y, Mo Z, Tan J W. A novel hierarchical data association with dynamic viewpoint model for multiple targets tracking. Journal of Visual Communication & Image Representation, 2016; 34: 37–49.
[16] Tchamova A, Dezert J, Semerdjiev T, Konstantinova P. Target tracking with generalized data association based on the general DSm rule of combination. Siam Journal on Control & Optimization, 2004; 49(2): 339–362.
[17] Kim C, Li F X, Ciptadi A, Rehg J M. Multiple hypothesis tracking revisited. In: 2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015; pp.4696–4704. doi: 10.1109/ICCV.2015.533
[18] Rasmussen C, Hager G D. Probabilistic data association methods for tracking complex visual objects. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2001; 23(6): 560–576.
[19] Gauvrit H, Cadre J P L, Jauffret C. A formulation of multitarget tracking as an incomplete data problem. IEEE Transactions on Aerospace & Electronic Systems, 1997; 33(4): 1242–1257.
[20] Yang F, Wang Y Q, Chen H, Zhang P Y. Adaptive collaborative Gaussian mixture probability hypothesis density filter for multi-target tracking. Sensors, 2016; 16(10):1666. doi: 10.3390/s16101666.
[21] Streit R L, Luginbuhl T E. Maximum likelihood method for probabilistic multihypothesis tracking. In: Proceedings of SPIE – The International Scociety for Optical Engineering, 1994; pp.394–405. doi: 10.1117/ 12.179066.
[22] Rezatofighi S H, Milan A, Zhang Z, Shi Q F, Dick A, Reid I. Joint probabilistic data association revisited. In: 2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2016; pp.3047–3055. doi: 10.1109/ICCV.2015.349.
[23] Li Y Y, Sun L Q, Zou Y B, Li Y. Individual pig object detection algorithm based on Gaussian mixture model. Int J Agric & Biol Eng, 2017; 10(5): 186–193
[24] Sun L Q, Li Z Y, Duan Q L, Sun X X. Automatic monitoring of pig excretory behavior based on motion feature. Sensor Letters, 2014; 12(3): 673–677. doi: 10.1166/sl.2014.3123.
[25] Li Y Y, Sun L Q, Sun, X X. Automatic tracking of pig feeding behavior based on particle filter with multi-feature fusion. Transactions of the CSAE, 2017; 33(Supp. 1): 246–252. (in Chinese).
[26] Matthews S G, Miller A L, Ploetz T, Kyriazakis I. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Scientific Reports, 2017; 7: 17582. doi: 10.1038/s41598-017-17451-6.
[27] Wang F L, Zhen Y, Zhong B N, Ji R R. Robust infrared target tracking based on particle filter with embedded saliency detection. Information Sciences, 2015; 301: 215–226.
[28] Mansouri M, Destain M F. An improved particle filtering for time-varying nonlinear prediction of biomass and grain protein content. Computers & Electronics in Agriculture, 2015; 114: 145–153.
[29] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2000; pp.142–149. doi: 10.1109/CVPR.2000.854761.
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Published
2021-07-31
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Sun, L., & Li, Y. (2021). Multi-target pig tracking algorithm based on joint probability data association and particle filter. International Journal of Agricultural and Biological Engineering, 14(4), 199–207. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6105
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Information Technology, Sensors and Control Systems
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