Resource allocation for tracking multiple targets using particle filters

Particle filters have been very widely used to track targets in video sequences. However, they suffer from an exponential rise in the number of particles needed to jointly track multiple targets. On the other hand, using multiple independent filters to track in crowded scenes often leads to erroneous results. We present a new particle filtering framework which uses an intelligent resource allocation scheme allowing us to track a large number of targets using a small set of particles. First, targets with overlapping posterior distributions and similar appearance models are clustered into interaction groups and tracked jointly, but independent of other targets in the scene. Second, different number of particles are allocated to different groups based on the following observations. Groups with higher associations (quantifying spatial proximity and pairwise appearance similarity) are given more particles. Groups with larger number of targets are given a larger number of particles. Finally, groups with ineffective proposal distributions are assigned more particles. Our experiments demonstrate the effectiveness of this framework over the commonly used joint particle filter with Markov Chain Monte Carlo (MCMC) sampling.