Adaptive earth movers distance-based Bayesian multi-target tracking

This study describes a complete system for multiple-target tracking in image sequences. The target appearance is represented as a set of weighted clusters in colour space. This is in contrast to the more typical use of colour histograms to model target appearance. The use of clusters allows a more flexible and accurate representation of the target, which demonstrates the benefits for tracking. However, it also introduces a number of computational difficulties, as calculating and matching cluster signatures are both computationally intensive tasks. To overcome this, the authors introduce a new formulation of incremental medoid-shift clustering that operates faster than mean shift in multi-target tracking scenarios. This matching scheme is integrated into a Bayesian tracking framework. Particle filters, a special case of Bayesian filters where the state variables are non-linear and non-Gaussian, are used in this study. An adaptive model update procedure is proposed for the cluster signature representation to handle target changes with time. The model update procedure is demonstrated to work successfully on a synthetic dataset and then on real datasets. Successful tracking results are shown on public datasets. Both qualitative and quantitative evaluations have been carried out to demonstrate the improved performance of the proposed multi-target tracking system. A higher tracking accuracy in long image sequences has been achieved compared to other standard tracking methods.