Anti-occlusion arithmetic for moving object tracking

A new method with occlusion for color-based object tracking is presented.The proposed technique employs mean shift iterations to derive the object candidate which is the most similar to a given object model, then uses Kalman filter to estimate the real states of the object.A color-based histogram with different weights that is robust for partial occlusion is selected as the target feature.The similarity between the target model and the candidates is expressed by a metric on the Bhattacharyya coefficient.An occlusion coefficient is proposed.When the object is occluded seriously,the observation cannot be used for updating by Kalman filter,the former state of the object is regarded as the current state.The simulation experiments show that the tracking is robust to partial and serious occlusion.