  We present a novel, real-time algorithm to track the trajectory of each
pedestrian in moderately dense crowded scenes. Our formulation is based on an
adaptive particle-filtering scheme that uses a combination of various
multi-agent heterogeneous pedestrian simulation models. We automatically
compute the optimal parameters for each of these different models based on
prior tracked data and use the best model as motion prior for our
particle-filter based tracking algorithm. We also use our "mixture of motion
models" for adaptive particle selection and accelerate the performance of the
online tracking algorithm. The motion model parameter estimation is formulated
as an optimization problem, and we use an approach that solves this
combinatorial optimization problem in a model independent manner and hence
scalable to any multi-agent pedestrian motion model. We evaluate the
performance of our approach on different crowd video datasets and highlight the
improvement in accuracy over homogeneous motion models and a baseline
mean-shift based tracker. In practice, our formulation can compute trajectories
of tens of pedestrians on a multi-core desktop CPU in in real time and offer
higher accuracy as compared to prior real time pedestrian tracking algorithms.
