Learning Support Correlation Filters for Visual Tracking
This work addresses the problem of real-time visual tracking for computer vision applications, offering an incremental improvement by combining SVM formulations with correlation filters for faster optimization.
The paper tackles the trade-off between accuracy and efficiency in visual tracking by proposing support correlation filters (SCFs), which achieve real-time performance with a computational complexity of O(n^2*logn) compared to O(n^4) for standard SVM-based methods, and show favorable results in accuracy and speed on benchmark datasets.
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency. Recently, the circulant matrix formed by dense sampling of translated image patches has been utilized in correlation filters for fast tracking. In this paper, we derive an equivalent formulation of a SVM model with circulant matrix expression and present an efficient alternating optimization method for visual tracking. We incorporate the discrete Fourier transform with the proposed alternating optimization process, and pose the tracking problem as an iterative learning of support correlation filters (SCFs) which find the global optimal solution with real-time performance. For a given circulant data matrix with n^2 samples of size n*n, the computational complexity of the proposed algorithm is O(n^2*logn) whereas that of the standard SVM-based approaches is at least O(n^4). In addition, we extend the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance. Experimental results on a large benchmark dataset show that the proposed SCF-based algorithms perform favorably against the state-of-the-art tracking methods in terms of accuracy and speed.