Robust object tracking based on adaptive multicue feature fusion

Abstract

Object tracking is challenging due to unconstrained variations in the target's appearance and complex environmental variations. Appearance models based on a single cue are inefficient in addressing the various tracking challenges. To address this, we propose a discriminative visual tracking approach in which complementary multicue features viz. RGB cue and histogram of gradient are integrated to build an efficient appearance model. The multicue feature fusion ensures that the limitations of the individual cue are suppressed and complementary multicue information are appropriately captured in the unified feature. These unified features are robust to scale variation, rotation, and background clutter. In addition, random forest classifier not only creates clear decision boundary between the foreground fragments and the background fragments but also aids in adaptive update of the reference dictionary. This adaptive update strategy avoids the eventual drift of the tracker during illumination variation, rotation, and deformation. Extensive qualitative and quantitative analyses on benchmark OTB-2015 and VOT2017 datasets demonstrate the robustness and accuracy of the proposed tracker against seven other state-of-the-art tracker.