TY - JOUR
T1 - On-Line Kernel-Based Tracking in Joint Feature-Spatial Spaces
JF - IEEE, 0-7695-2158-2164
Y1 - 2004
A1 - Yang,C.
A1 - Duraiswami, Ramani
A1 - Elgammal,A.
A1 - Davis, Larry S.
AB - We will demonstrate an object tracking algorithm thatuses a novel simple symmetric similarity function between spatially-smoothed kernel-density estimates of the model and target distributions. 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 max- imizing this similarity function. To alleviate the quadratic complexity of the density estimation, we employ Gaussian kernels and the fast Gauss transform to reduce the compu- tations to linear order. This leads to a very efficient and robust nonparametric tracking algorithm. More details can be found in [2]. The system processes online video stream on a P4 1.4GHz and achieves 30 frames per second using an ordinary webcam.
ER -