Most mean-shift based object tracking algorithms neglected information on the spatial distribution of dense features. This study uses dense features to enhance the reliability of tracking. Some color features gather on tracking objects

and each feature forms a region of certain size. These dense feature regions play an important role in human vision. Information regarding the spatial structures of these dense feature regions can be used in object tracking. An effective and efficient tracking object model is presented. Intensive features are found

and the areas and distances between the dense region centroids and the target object center are calculated to obtain the weight of each feature

which is applied to describe the tracked object. The intensive features in the target model are heavier than the discrete features. Simultaneously

the zero-order moment and the similarity coefficient between the target model and candidate models are used to estimate the target area. Subsequently

an area compensation method is used to compensate the object areas that are weakened by background weighting. Finally

the estimated area and the second-order center moment are used to adaptively estimate the object scale and direction. The object models are updated when the background shows significant changes. Experimental results show that the proposed method can adapt well to the object scale changes

with an average tracking accuracy of >94.6%. In addition

the proposed method has higher accuracy

efficiency

and robustness than some state-of-the-art methods. The proposed method increases the weight difference between different features in the target model. This method is efficient indistinguishing the target from the background. The area compensation method solves the problem wherein the estimated target area is less than the actual area because of the weakened target feature weight.