Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. * Corresponding author to explore an effective tracking algorithm which can be designed to be either discriminative [17, 9, 13, 15, 22, 1, 31] or generative [24, 18, 16, 33] models. It seeks to design a robust classifier or filter to detect the target, and establish an optimal mechanism to update the model at each frame. The other one is to exploit the power of the target representation which may come from conventional handcraft features [16, 18, 17, 9, 24] or high-level convolutional features [15, 28, 23, 27, 21] from deep Convolutional Neural Networks (CNNs). These methods improve performance significantly from different aspects. However, to further improve the performance by more complex tracking algorithms or features, it would undoubtedly increase the computational complexity, which would limit the real-time performance of visual tracking.
Wang et al. REF proposed a large margin visual tracking method with circulant feature maps, which employed a multi-modal detection technique to avoid tracking drift.
Large Margin Object Tracking with Circulant Feature Maps
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science