A SEMI AUTOMATIC TARGET INITIALIZATION METHOD BASED ON VISUAL SALIENCY
First Claim
1. A method for semi automatic target initialization (100) fundamentally comprises the following steps,image coordinate given by user (101),initial window selection via CSD (102),saliency map generation (103),binarization (104)target selection (105),output, bounding box enclosing the target (106).
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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
15 Claims
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1. A method for semi automatic target initialization (100) fundamentally comprises the following steps,
image coordinate given by user (101), initial window selection via CSD (102), saliency map generation (103), binarization (104) target selection (105), output, bounding box enclosing the target (106).
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2. In the method for semi automatic target initialization (100), the step of “
- initial window selection via CSD (102)”
further comprises the sub-steps of;given coordinate input (201), obtain foreground histogram (202), obtain background histogram (203), calculate quadratic-chi histogram distance (204), Obtain histogram distance vector by repeating steps 202,203 and 204 for each window size (205), select 1st local maximum of histogram distance vector as initial window size (206).
- initial window selection via CSD (102)”
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3. In the method for semi automatic target initialization (100), the step of “
- saliency map generation (103)”
further comprises the sub-steps of;given initial window (301), calculate mean intensity for patches obtained by any super pixel selection algorithm (302), calculate mean intensity difference in eight neighborhood and assign as weights (303), calculate shortest path from each foreground patch to a background patch (304), return saliency map by normalizing the values calculated as shortest path (305).
- saliency map generation (103)”
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4. In the method for semi automatic target initialization (100), the step of “
- binarization (104)”
further comprises the sub-steps of;given saliency map (401), exploit local maxima of saliency map and sort in descending order to obtain LocalMaxsorted (402), calculate normalized laplacian of LocalMaxsorted (403), obtain threshold by calculating weighted average of LocalMaxsorted (404), binarize saliency map with threshold (405).
- binarization (104)”
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5. In the method for semi automatic target initialization (100), the step of “
- target selection (105)”
further comprises the sub-steps of;execute connected component analysis (501), calculate regularization energy of each connected component (502), report the bounding box of the connected component as target having the most salient region with minimum distance to center (503),
- target selection (105)”
Specification