Similarity Measure for Nonparametric Kernel Density Based Object Tracking

An object tracking algorithm using 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 that 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. Several sequences are placed at http://www.cs.umd.edu/users/yangcj/node3.html.