Abstract
Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.
Highlights
Visual object tracking remains as an active research topic in computer vision that yields a variety of applications, such as intelligent video surveillance, human computer interaction, traffic control, and medical image analysis [1,2,3]
In order to estimate the scale of the target object more accurate and efficient, we propose to use the water flow driven minimum barrier distance (MBD) algorithm [27] to detect object on an image patch centered at the estimated position of the target object
In order to deal with the scale variations, we proposed to use the water flow driven MBD algorithm [27] to detect object on an image patch centered at the estimated position of the target object
Summary
Visual object tracking remains as an active research topic in computer vision that yields a variety of applications, such as intelligent video surveillance, human computer interaction, traffic control, and medical image analysis [1,2,3]. The target object may be challenged by a variety of complicated intrinsic and extrinsic factors, such as scale variation, deformation, in-plane and out-of-plane rotations, and most existing correlation filter based trackers either use a sole filtering template or a fixed scale to represent the target object, these trackers cannot effectively capture appearance variations of target object [22,23]. Due to the high computational burden caused by modeling complex appearance models and estimating the scale of target object, most existing correlation filter based trackers could not run in real-time, which bring a lot of inconvenience in their applications [24,25].
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