Semi automatic target initialization method based on visual saliency
First Claim
1. A method for semi automatic target initialization (100) comprises the steps of,inputting an image coordinate by user (101),selecting an initial window around the image coordinate by center-surround histogram distance (102),generating saliency map of the window (103),binarizing the saliency map (104),selecting a target (105),outputting a bounding box enclosing the target (106) and characterized by the following steps for binarizing the saliency map;
- detecting local maxima of the saliency map and sorting them in descending order to obtain a vector LocalMaxsorted (402),calculating a normalized laplacian of the vector with the formula;
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Abstract
Target initialization can dramatically change the performance of the tracker, since the initial window determines for the tracker what to track. In order to achieve a better tracking performance; The present invention relates to a method of semi automatic target initialization based on visual saliency for a given point coordinate in the vicinity of target by the user. Performance boost of tracker is mainly based on two key features of target initialization algorithm: It is capable of compensating erroneous user input; also selecting the most distinctive, salient part of object as target, so better discrimination is achieved between the target and background. Experimental results show that tracking performance is boosted in scenarios, in which the tracking is initialized by the proposed algorithm. Very low computational cost and requirement of only a point coordinate as input in the neighborhood of the target make this approach preferable in real time tracking applications.
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Citations
8 Claims
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1. A method for semi automatic target initialization (100) comprises the steps of,
inputting an image coordinate by user (101), selecting an initial window around the image coordinate by center-surround histogram distance (102), generating saliency map of the window (103), binarizing the saliency map (104), selecting a target (105), outputting a bounding box enclosing the target (106) and characterized by the following steps for binarizing the saliency map; -
detecting local maxima of the saliency map and sorting them in descending order to obtain a vector LocalMaxsorted (402), calculating a normalized laplacian of the vector with the formula; - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification