Robust visual tracking for planar objects using gradient orientation pyramid.

Direct visual tracking (DVT) for planar objects is a fundamental problem in computer vision. DVT methods often formulate tracking as an image registration problem, where image intensities are directly used to match two images. However, these methods are usually sensitive to illumination changes as they assume intensities are constant. The gradient orientation (GO) was proven to be insensitive to illumination variations in previous reports. We further confirmed that the GO's robustness can be significantly improved when the pyramid technique is employed. We present a robust DVT method, named gradient orientation pyramid efficient second-order minimization (GOP-ESM), based on the proposed gradient orientation pyramid descriptor. GOP-ESM takes the advantages of the robust feature descriptors and the efficient second-order minimization method as to enhance tracking robustness and accuracy. We also published a tracking dataset for planar objects with illumination changes (POIC). The evaluations on the proposed POIC dataset and the other two public benchmark datasets demonstrated that GOP-ESM outperforms the state-of-the-art tracking methods against various environmental variations, especially illumination changes. (C) 2019 SPIE and IS&T