A Novel Algorithm Based on a Common Subspace Fusion for Visual Object Tracking

Abstract

Recent methods for visual tracking exploit a multitude of information obtained from combinations of handcrafted and/or deep features. However, the response maps derived from these feature combinations are often fused using simple strategies such as winner-takes-all or weighted sum approaches. Although some efficient fusion methods have also been proposed, these methods still do not leverage the individual strengths of the different features being fused. In the current work, we propose a novel information fusion strategy comprising a common low-rank subspace for the fusion of different types of features and tracker responses. Firstly, we interpret the response maps as smoothly varying functions which can be efficiently represented using individual low-rank matrices, thus removing high frequency noise and sparse artifacts. Secondly, we estimate a common low-rank subspace which is constrained to remain close to each individual low-rank subspace resulting in an efficient fusion strategy. The proposed algorithm achieves good performance by integrating the information contained in heterogeneous feature types. We demonstrate the efficiency of our algorithm using several combinations of features as well as correlation filter and end-to-end deep trackers. The proposed common subspace fusion algorithm is generic and can be used to efficiently fuse the response maps of varying types of feature representations as well as trackers. Extensive experiments on several tracking benchmarks including OTB100, TC128, VOT-ST 2018, VOT-LT 2018, UAV123, GOT-10K and LaSoT have demonstrated significant performance improvements compared to many SOTA tracking methods.

Keywords

• Visual object tracking
• features fusion
• correlation filters
• deep features