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Vol. 169, Issue 4, April 2014, pp. 25-32

 

Bullet

 

Scale-adaptive Local Patches for Robust Visual Object Tracking
 

Kang Sun, Xinwei Li

School of Electrical Engineering and Automation, Henan Polytechnic University Jiaozuo, 454000, China
E-mail: sunkanghpu@163.com

 

 

Received: 21 January 2014 /Accepted: 7 March 2014 /Published: 30 April 2014

Digital Sensors and Sensor Sysstems

 

Abstract: This paper discusses the problem of robustly tracking objects which undergo rapid and dramatic scale changes. To remove the weakness of global appearance models, we present a novel scheme that combines objectís global and local appearance features. The local feature is a set of local patches that geometrically constrain the changes in the targetís appearance. In order to adapt to the objectís geometric deformation, the local patches could be removed and added online. The addition of these patches is constrained by the global features such as color, texture and motion. The global visual features are updated via the stable local patches during tracking. To deal with scale changes, we adapt the scale of patches in addition to adapting the object bound box. We evaluate our method by comparing it to several state-of-the-art trackers on publicly available datasets. The experimental results on challenging sequences confirm that, by using this scale-adaptive local patches and global properties, our tracker outperforms the related trackers in many cases by having smaller failure rate as well as better accuracy.

 

Keywords: Scale adaptiveness, Local patch, Global feature, Visual tracking, Robustness.

 

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