Robust regression and outlier detection
Robust regression and outlier detection
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
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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.