Record number :
1734667
Title of article :
Robust segment-based object tracking using generalized hyperplane approximation
Author/Authors :
Choi، نويسنده , , Hyun-Chul and Oh، نويسنده , , Se-Young، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
12
From page :
2980
To page :
2991
Abstract :
Tracking based on gradient descent algorithm using image gradient is one of the popular object tracking method. However, it easily fails to track when illumination changes. Although several illumination invariant features have been proposed, applying the invariant feature to the gradient descent method is not easy because the invariant feature is represented as a non-linear function of image pixel values and its Jacobian cannot be calculated in a closed-form. To make it possible, we introduce the generalized hyperplane approximation technique and apply it to histogram of oriented gradient (HOG) feature, one of the well-known illumination invariant feature. In addition, we achieve partial occlusion invariance using image segments. The hyperplanes are calculated from training segment images obtained by perturbing the motion parameter around the target region. Then, it is used to map the difference in non-linear feature of image onto the increment of alignment parameters. This process is mathematically same to the gradient descent method. The information from each segment is integrated by a simple weighted linear combination with confidence weights of segments. Compared to the previous tracking algorithms, our method shows very fast and stable tracking results in experiments on several practical image sequences.
Keywords :
object tracking , Generalized hyperplane approximation , Partial occlusion , illumination invariance , Histogram of oriented gradient
Journal title :
PATTERN RECOGNITION
Journal title :
PATTERN RECOGNITION
Serial Year :
2012
Link To Document :
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