Robust object tracking with the inverse relocation strategy

Abstract

Robust object tracking is a challenging task in multimedia understanding and computer vision. The traditional tracking algorithms only use the forward tracking information while neglecting the inverse information. An inverse relocation strategy is used to learn the translation and scale filters in the proposed tracking algorithm. To begin with, we learn a translation filter using both the forward and the inverse tracking information based on the ridge regression. The object position can be attained using the translation filter by the inverse relocation strategy. Secondly, the scale filter can be attained using the ridge regression and a smooth strategy is adopted to integrate the forward and inverse scale factors. Experiments are performed on the scale variation dataset and the OTB-50 dataset. Extensive experimental results show that the proposed algorithm performs favorably against several state-of-the-art methods in terms of precision and success rate. Meanwhile, the proposed algorithm is also robustness to the deformation to a great extent.