Visual Object Tracking Based on Light Field Imaging in the Presence of Similar Distractors

Identifying moving target from nearby similar distractors is a crucial challenge in visual object tracking due to the lack of angular information. In this work, we propose a visual object tracking method based on 4D light field imaging which captures abundant spatial and angular information in a single snapshot. Our method first presents a novel image representation of epipolar plane images (EPIs) attained from light field multi-view images, i.e., the macro-epipolar plane image (macro-EPI), to highlight both spatial and angular information of the objects. On this basis, we design a feature extractor for macro-EPI to learn the visual angle information. Thereafter, we design an object tracking framework based on a two-pattern convolution filters method which merges the confidence scores of macro-EPI and center view image. Experimental results demonstrate that the proposed method successfully discriminates the target from the similar distractors and achieves better performance than state-of-the-art visual object tracking methods.