Sequential generation for visual tracking

A novel variant of particle filters is presented, where new particles are generated sequentially by adapting the proposal density dynamically according to the likelihood of the current particle which are just generated. The new algorithm is able to capture more nonlinear motion and produce a better localization of the moving target in an efficient way. Experiments on both synthetic and real-world data verify its effectiveness and demonstrate its superiority over the generic particle filter.