Multiple object tracking via multi-layer multi-modal framework

In this paper, we propose a new multiple object tracking method via multi-layer multi-modal framework. To handle erroneous merge and labeling problem in multiple object tracking, we use a multi layer representation of dynamic Bayesian network and modified sampling method. For robust visual tracking, our dynamic Bayesian network based tracker fuses multi-modal features such as color and edge orientation histogram. The proposed method was evaluated under several real situations and promising results were obtained.