Abstract
Tracking methods based on a correlation filter have attracted much attention because of their high efficiency and strong robustness. However, a tracker based on a single feature is obviously not sufficient to adapt to the complex appearance changes of the target. Besides, rapid and exact scale estimation is still a challenging problem in the field of visual tracking. In this paper, we introduce an independent scale filter for the estimation of the scale of an object and merge two complementary features to further boost the performance of the tracker. At the same time, a dimension reduction strategy is adopted to decrease the computational load. Finally, a dynamic learning rate-based model update mechanism is inserted to effectively alleviate model degradation problem by suppressing the influence of noisy appearance changes. The extensive experiments were conducted on the object tracking benchmark (OTB) dataset and Temple color 128 dataset. The quantitative and qualitative results exhibit that compared with other popular trackers, the tracker proposed in this paper acquires favorable results in tracking accuracy, efficiency, and robustness. On the OTB-2015 benchmark dataset, it obtains precision scores of 0.773, 0.782, and 0.714 and success scores of 0.585, 0.606, and 0.534 in the three indexes of OPE, TRE, and SRE. On the Temple color 128 dataset, it acquires precision scores of 0.641, 0.681, and 0.606 and success scores of 0.478, 0.515, and 0.445 in the three indexes of OPE, TRE, and SRE, surpassing many well-known tracking methods. In terms of tracking efficiency, it runs at a speed of 42.3 frames/s on a single CPU, making it suitable for real-time applications.
Highlights
As an important issue in computer vision area, visual object tracking plays a critical role in video monitoring, automatic driving, video analysis, and other real-world applications
We firstly introduce a feature dimension reduction strategy to improve the efficiency of the proposed approach and utilize a discriminative correlation filter to estimate the size of the object
ON THE object tracking benchmark (OTB)-2015 1) EXPERIMENT 1: FAST SCALE KERNELIZED CORRELATION FILTER Here, we present experiments to verify the effectiveness of two components, namely scale estimation and dimension reduction
Summary
As an important issue in computer vision area, visual object tracking plays a critical role in video monitoring, automatic driving, video analysis, and other real-world applications. Great progress has been achieved in recent years, it is still very hard to develop a robust tracking algorithm due to the influence of multiple challenges such as illumination change, background clutters, scale variation, deformation and occlusion, which are caused by random variation in video sequences. Ross et al [2] present an incremental visual tracking (IVT) method, which learns a low dimensional principal component analysis (PCA) subspace and updates the PCA subspace online. The IVT method can handle illumination and pose changes, it is sensitive to outliers (such as partial occlusion and background clutter). Kwon and Lee [5] adopt a sparse PCA method to select multiple color and edge templates, which is robust to the challenges of illumination variation, scale change and non-rigid motion.
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