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Vol. 177, Issue 8, August 2014, pp. 85-91

 

Bullet

 

Improved Mean Shift for Robust Object Tracking
 

Hongying Zhang, Zheng Hu

Civil Aviation University of China, Tianjin, 300300, China

E-mail: carole_zhang0716@163.com, zhu_guyue@163.com

 

 

Received: 20 March 2014 /Accepted: 31 July 2014 /Published: 31 August 2014

Digital Sensors and Sensor Sysstems

 

Abstract: In this paper, we present an improved mean shift for robust object tracking in complex environment. Traditional RGB color model used in mean shift tracker is sensitive to interference from similar background. In order to solve this problem, a new saliency-color target model is proposed through using the state-of-the-art target representation and updated background-weighed method. In addition, traditional mean shift method using fixed tracking window may cause tracking errors when target becomes close to or far away from the camera. Therefore, tracking window with self- adjust scheme is proposed in this paper. The tracking region parameters are updated through affine transform of feature corner datasets between adjacent frames. Moreover, a new prediction strategy is utilized to track the target with fast motion and partial occlusion. Experiment results demonstrate the effectiveness of proposed method, which can track object robustly under similar background, size changing, partial occlusion, etc.

 

Keywords: Mean shift, Target model, Scale and orientation, Partial occlusion, Robust tracking.

 

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