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Mean shift tracking through scale and occlusion

Mean shift tracking through scale and occlusion

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© The Institution of Engineering and Technology
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This study describes a method for tracking objects through scale and occlusion. The technique presented is based on the mean shift algorithm, which provides an efficient way to track objects based on their colour characteristics. A novel and efficient method is derived for tracking through changes in the target scale, where an object of interest moves away or towards the camera and therefore appears to change size in the image plane. The method works by interleaving spatial mean shift iterations with scale iterations. It is shown that this method is considerably more efficient than other methods and possesses other advantages too. It is also demonstrated that the Bhattacharyya coefficient, a histogram similarity metric that is used in the mean shift framework, can be used to reliably detect when target occlusion occurs. In such situations, the motion of an object can be extrapolated to give an accurate estimate of its position. This is used as the basis of a technique for tracking through occlusion. Experimental results are presented on data from various scenarios.

Inspec keywords: image colour analysis; extrapolation; image motion analysis; cameras; object tracking

Other keywords: Bhattacharyya coefficient; image plane; mean shift algorithm; occlusion; spatial mean shift iteration; extrapolation; scale iteration; camera; object tracking; colour characteristics; histogram similarity metric; position estimate; object motion; mean shift tracking

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis)

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