Condensation algorithm can deal with non-Gaussian, nonlinear visual contour tracking in a unified way. Despite its simple implementation and generality, it has two main limitations. The first limitation is that in sampling stage the algorithm does not take advantage of the new measurements. As a result of the inefficient sampling strategy, the algorithm needs a large number of samples to represent the posterior distribution of state. The next is in selection step, resampling may introduce the problem of sample impoverishment. To address these two problems, we present an improved visual tracker based on importance sampling/resampling algorithm. Gaussian density of each sample is adopted as sub-optimal importance proposal distribution, which can steer the samples towards the high likelihood by considering the latest observations. We also adopt a criterion of effective sample size to determine whether the resampling is necessary or not. Experiments with real image sequences show that the performance of new algorithm improves Considerably for tracking in visual clutter.