1 research outputs found
Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
Visual object tracking is one of the major challenges in the field of
computer vision. Correlation Filter (CF) trackers are one of the most widely
used categories in tracking. Though numerous tracking algorithms based on CFs
are available today, most of them fail to efficiently detect the object in an
unconstrained environment with dynamically changing object appearance. In order
to tackle such challenges, the existing strategies often rely on a particular
set of algorithms. Here, we propose a robust framework that offers the
provision to incorporate illumination and rotation invariance in the standard
Discriminative Correlation Filter (DCF) formulation. We also supervise the
detection stage of DCF trackers by eliminating false positives in the
convolution response map. Further, we demonstrate the impact of displacement
consistency on CF trackers. The generality and efficiency of the proposed
framework is illustrated by integrating our contributions into two
state-of-the-art CF trackers: SRDCF and ECO. As per the comprehensive
experiments on the VOT2016 dataset, our top trackers show substantial
improvement of 14.7% and 6.41% in robustness, 11.4% and 1.71% in Average
Expected Overlap (AEO) over the baseline SRDCF and ECO, respectively.Comment: Published in ACCV 201