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 tr...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.展开更多
In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low...In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low accuracy of the tracking target.In this study,a multi-channel color feature adaptive fusion algorithm was proposed,and the target scale of the pig was updated in real-time by utilizing the contour information of the target pig.Experiments show that the proposed algorithm had a distance precision of 89.7%and an overlap precision of 87.5%,and the average running speed of this algorithm was 50.1 fps.The robustness of the proposed algorithm in tracking target deformation and scale variation were significantly improved,which satisfies the accuracy and real-time requirements of pig target tracking.展开更多
基金This work was supported by the National High Technology Research and Development Program(863 Plan)(Grant No.2013AA102306).
文摘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.
基金This work was supported in part by the National Key Research and Development Plan for the 13th Five-Year Plan under Grant 2016YFD0700200This work was supported in part by the National High Technology Research and Development Program of China(2013AA102306).
文摘In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low accuracy of the tracking target.In this study,a multi-channel color feature adaptive fusion algorithm was proposed,and the target scale of the pig was updated in real-time by utilizing the contour information of the target pig.Experiments show that the proposed algorithm had a distance precision of 89.7%and an overlap precision of 87.5%,and the average running speed of this algorithm was 50.1 fps.The robustness of the proposed algorithm in tracking target deformation and scale variation were significantly improved,which satisfies the accuracy and real-time requirements of pig target tracking.
