Object tracking under large motion: Combining coarse-to-fine search with superpixels

Abstract

We propose an object tracking method under large motion in image sequences. Dense sampling and particle filtering have been widely applied to cope with this problem; however, the former is computationally expensive, and the latter is sensitive to local minima. By introducing a novel search method based on coarse-to-fine strategy and image superpixels, we try to solve both drawbacks. In the coarse step, we first extract superpixels associated with a target object on the entire search region by using a simple generative appearance model. In the fine step, we perform a sampling and similarity measurement process within the selected superpixels to find the most accurate location of the target object, also suggest a way to use both a discriminative appearance model and a sophisticated generative appearance model simultaneously. Extensive experiments on popular benchmark dataset demonstrate that the proposed method outperforms other competitive approaches, and also show better results in challenging scenarios such as occlusion, deformation, out-of-view, and in-plane/out-of-plane rotation.