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Attributed Graphs for Tracking Multiple Objects in Structured Sports Videos
2015
2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
In this paper we propose a novel approach for tracking multiple object in structured sports videos using graphs. The objects are tracked by combining particle filter and frame description with Attributed Relational Graphs. We start by learning a probabilistic structural model graph from annotated images and then use it to evaluate and correct the current tracking state. Different from previous studies, our approach is also capable of using the learned model to generate new hypotheses of where
doi:10.1109/iccvw.2015.102
dblp:conf/iccvw/MorimitsuCB15
fatcat:ewts6i7rkvdefl5qalxkquyioy