Particle filters have proven to be an effective tool for visual tracking in non-gaussian, cluttered environments. Conventional particle filters however do not scale to the problem of Human Motion Capture (HMC) because of the large number of degrees of freedom involved. Annealed Particle Filtering (APF), introduced by Deutscher et al [3], tackled this by layering the search space and was shown to be a very effective tool for HMC. In this paper we improve upon and extend the APF in two ways. First we develop a hierarchical search strategy which automatically partitions the search space without any explicit representation of the partitions. Then we introduce a crossover operator (similar to that found in Genetic Algorithms) which improves the ability of the tracker to search different partitions in parallel. We present results for a simple example to demonstrate the new algorithm?s implementation and then apply it to the considerably more complex problem of Human Motion Capture with 34 degrees of freedom.