Visual object tracking based on filtering methods
Kun Wang, Xiaoping P. Liu · 2011
Visual object tracking is an important, but open research topic in many practical applications. In this paper, the particle filter, a filtering algorithm based on the sequential-importance-sampling (SIS), is developed and implemented with different modifications to the transition models and constraint conditions. By applying the particle filter into a typical object tracking task, several experimental results are obtained and the feasibility of the modified particle filter is verified.