Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation

Abstract

Boosted by large and standardized benchmark datasets, visual object tracking has made great progress in recent years and brought about many new trackers. Among these trackers, correlation filter based tracking schema exhibits impressive robustness and accuracy. In this work, we present a fully functional correlation filter based tracking algorithm which is able to simultaneously model target appearance changes from spatial displacements, scale variations, and rotation transformations. The proposed tracker first represents the exhaustive template searching in the joint scale and spatial space by a block-circulant matrix. Then, by transferring the target template from the Cartesian coordinate system to the Log-Polar coordinate system, the circulant structure is well preserved for the target even after whole orientation rotation. With these novel representation and transformation, object tracking is efficiently and effectively performed in the joint space with fast Fourier Transform. Experimental results on the VOT 2015 benchmark dataset demonstrate its superior performance over state-of-the-art tracking algorithms.