Improving NCC-Based Direct Visual Tracking

之前找这篇论文花了很大的功夫，最后是知乎上一位很Nice的知友邮箱发我的，特分享之：

链接：https://pan.baidu.com/s/1mKg9H7gQ_KO7-2bWZ-R6vw 
提取码：c21n 
 

 

Abstract

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.

Keywords

Mutual Information Augmented Reality Reference Image Visual Tracking Illumination Change