The appearance model and features of objects are commonly used for object matching.In long-term tracking

having large variations in scales

shape deformation

and other noises

it would be very challenging to success-fully keep tracking in this way.An effective object appearance model is proposed

which can improve the efficiency and effectiveness of object tracking. Image cues are used to describe the object appearance in this method.After image segmentation

the information is extracted from the superpixels (each segmentation block represents one superpixel).Then their SIFT descriptors are clustered to form a codebook.The weight of each word in the codebook is calculated to construct the target model to filter the superpixel points.Next the pyramidal Lucas-Kanade tracker is used to predict the location of the superpixel points in the next frame and move the tracking window. Combined with the weighting of point displacement

can conquer the variations in scales and shape deformation can be handled. Experimental results show that the proposed method has good and robust performance even with appearance deformation and illumination changes.