Abstract
We propose a novel framework that jointly estimates the ground plane and a target's motion trajectory. This results in improvements for both. Estimating their joint posterior is based on Particle Markov Chain Monte Carlo (Particle MCMC). In Particle MCMC, the best target state is inferred by a particle filter and the best ground plane is obtained by MCMC. Compared with conventional sampling methods that iteratively infer the best target states and ground plane parameters, our method infers them jointly. This reduces sampling errors drastically. Experimental results demonstrate that our method outperforms several state-of-the-art tracking methods, while the ground plane accuracy is also improved.
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