Target Tracking Based on Adaptive Particle Filter

This paper presents a method that can track non-rigid moving objects using adaptive particle filter based on spatiograms. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in target tracking. Two key components of particle filters are observation models and motion models. Firstly, because the observation model based on general color histograms discards the spatial information of images, the accuracy of the observation model is decreased. We adopt a proper observation model based on spatiograms which are histograms augmented with spatial means and covariances to capture a richer description of targets and increase robustness in tracking. Secondly, approximate fixed motion models used in practice, such as unrestricted random walking model with fixed noise variance, are not accurate enough. To overcome this problem, we adopt the adaptive multivariate autoregressive models which are computed via the regression analysis. The proposed adaptive motion models can adjust the model order, process noise variance and model parameters automatically. Also, the number of particles is adjusted automatically. The experiments show that the proposed algorithm can effectively track moving objects and increase the robustness in tracking. Its performance is compared with that of the general particle filtering algorithm to demonstrate the advantages of the new method.