Robust segment-based object tracking using generalized hyperplane approximation

Title
Robust segment-based object tracking using generalized hyperplane approximation
Author(s)
최현철Oh, Se-Young[Oh, Se-Young]
Keywords
FEATURES; MODELS
Issue Date
201208
Publisher
ELSEVIER SCI LTD
Citation
PATTERN RECOGNITION, v.45, no.8, pp.2980 - 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. (C) 2012 Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/27481http://dx.doi.org/10.1016/j.patcog.2012.02.008
ISSN
0031-3203
Appears in Collections:
공과대학 > 전자공학과 > Articles
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