%0 Journal Article %1 Harmsen20097 %A Harmsen, Stephan R. %A Koenderink, Nicole J.J.P. %D 2009 %J Computers and Electronics in Agriculture %K Multi-targettracking %N 1 %P 7 - 18 %R DOI: 10.1016/j.compag.2008.07.004 %T Multi-target tracking for flower counting using adaptive motion models %U http://www.sciencedirect.com/science/article/B6T5M-4TBVPMX-1/2/330273d70342196926b1b53666fbfa24 %V 65 %X Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.

@article{Harmsen20097, abstract = {Counting the number of flowers in a plant is an example of agricultural quality inspection issues in which a simple 2D image of the product does not suffice. It is essential to see the object under inspection from multiple viewpoints to get a clear estimation of the quality of the product. In order to use multiple viewpoints to obtain a proper quality assessment, a multi-target tracking algorithm that accurately identifies relevant features of the product under inspection is proposed in this paper. The approach is illustrated with an experiment in which the flowers in a number of plants are counted. For the presented method, the plant rotates in front of a camera and a number of consecutive images is taken. The tracking algorithm detects, predicts, and matches the (partially occluded) flowers in the image. The experiments provide a proof of principle of the proposed method. The conclusion of this paper is that the presented multi-target tracking algorithm can be used to solve many similar quality assessment issues for agricultural objects.}, added-at = {2009-05-19T13:37:13.000+0200}, author = {Harmsen, Stephan R. and Koenderink, Nicole J.J.P.}, biburl = {https://www.bibsonomy.org/bibtex/211c9b73f2b36d81077f5534cd3d0b97b/midtiby}, doi = {DOI: 10.1016/j.compag.2008.07.004}, file = {:D\:\\Dokumenter\\Articles\\pdf\\2009 Multi-target trackingnext term for flower counting using adaptive motion models.pdf:PDF}, interhash = {625b95411df43be7dcc2b12b56b8ca5a}, intrahash = {11c9b73f2b36d81077f5534cd3d0b97b}, issn = {0168-1699}, journal = {Computers and Electronics in Agriculture}, keywords = {Multi-targettracking}, number = 1, pages = {7 - 18}, timestamp = {2009-05-19T13:37:13.000+0200}, title = {Multi-target tracking for flower counting using adaptive motion models}, url = {http://www.sciencedirect.com/science/article/B6T5M-4TBVPMX-1/2/330273d70342196926b1b53666fbfa24}, volume = 65, year = 2009 }