Learning an Orientation and Scale Adaptive Tracker with Regularized Correlation Filters

Spatial and temporal constraints are very important for correlation filter (CF)-based trackers. However, the existing methods usually fail to regularize the CF learning by the spatio-Temporal information because of the ineffective target representation. To address the issue, we propose a novel Orientation and Scale adaptive tracker with Regularized Correlation Filters (OSRCF) for visual tracking. First, we directly employ the target region to build a spatio-Temporally regularized CF, which is solved efficiently by the alternating direction method of multipliers (ADMM). Especially, the spatial regularization module can alleviate boundary effects by suppressing the outside background, while the temporal one can handle rapid appearance changes by smoothing the CF updating. Second, we obtain a non-Axis-Aligned bounding box for the target region representation by orientation and scale adaptive strategy, which cooperates a straightforward orientation estimation with a discriminative scale space correlation filter. We perform comprehensive experiments on two recent visual tracking benchmark datasets: VOT2017 and OTB2015. The results show that our OSRCF tracker outperforms top-ranked methods with handcrafted features in VOT2017 and achieves outstanding performance compared to some state-of-The-Art methods in OTB2015.