We propose a dynamic resource allocation algorithm based on Ranking Support Vector Machine (R-SVM) [1] for particle filter tracking. We adjust the number of observations in each frame adaptively and automatically, where tracker performs measurement for a subset of highly ranked particles in likelihood to preserve mode locations in the posterior and allocates the rest of particles to maintain the diversity of the posterior without actual measurements.
.