Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Multi-Sensor Image Alignment
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy
International Journal of Computer Vision
Homography-based 2D Visual Tracking and Servoing
International Journal of Robotics Research
Image registration using robust M-estimators
Pattern Recognition Letters
Image alignment and stitching: a tutorial
Foundations and Trends® in Computer Graphics and Vision
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
A dataset and evaluation methodology for template-based tracking algorithms
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Generalizing Inverse Compositional and ESM Image Alignment
International Journal of Computer Vision
Real-time Quadrifocal Visual Odometry
International Journal of Robotics Research
International Journal of Computer Vision
Least squares quantization in PCM
IEEE Transactions on Information Theory
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Direct visual tracking can be impaired by changes in illumination if the right choice of similarity function and photometric model is not made. Tracking using the sum of squared differences, for instance, often needs to be coupled with a photometric model to mitigate illumination changes. More sophisticated similarities, e.g. mutual information and cross cumulative residual entropy, however, can cope with complex illumination variations at the cost of a reduction of the convergence radius, and an increase of the computational effort. In this context, the normalized cross correlation (NCC) represents an interesting alternative. The NCC is intrinsically invariant to affine illumination changes, and also presents low computational cost. This article proposes a new direct visual tracking method based on the NCC. Two techniques have been developed to improve the robustness to complex illumination variations and partial occlusions. These techniques are based on subregion clusterization, and weighting by a residue invariant to affine illumination changes. The last contribution is an efficient Newton-style optimization procedure that does not require the explicit computation of the Hessian. The proposed method is compared against the state of the art using a benchmark database with ground-truth, as well as real-world sequences.