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Real-Time Kernel-Based Tracking in Joint Feature-Spatial Spaces

dc.contributor.authorYang, Changjiangen_US
dc.contributor.authorDuraiswami, Ramanien_US
dc.contributor.authorElgammal, Ahmeden_US
dc.contributor.authorDavis, Larryen_US
dc.description.abstractAn object tracking algorithm that uses a novel simple symmetric similarity function between spatially-smoothed kernel-density estimates of the model and target distributions is proposed and tested. The similarity measure is based on the expectation of the density estimates over the model or target images. The density is estimated using radial-basis kernel functions which measure the affinity between points and provide a better outlier rejection property. The mean-shift algorithm is used to track objects by iteratively maximizing this similarity function. To alleviate the quadratic complexity of the density estimation, we employ Gaussian kernels and the fast Gauss transform to reduce the computations to linear order. This leads to a very efficient and robust nonparametric tracking algorithm. The proposed algorithm is tested with several image sequences and shown to achieve robust and reliable real-time tracking. (UMIACS-TR-2004-12)en_US
dc.format.extent338189 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4567en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2004-12en_US
dc.titleReal-Time Kernel-Based Tracking in Joint Feature-Spatial Spacesen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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