Robust and Real-Time Visual Tracking Based on Single-Layer Convolutional Features and Accurate Scale Estimation

Visual tracking is a fundamental problem in computer vision. Recently, some methods have been developed to utilize features learned from a deep convolutional neural network for visual tracking and achieve record-breaking performances. However, deep trackers suffer from efficiency. In this paper, we propose an object tracking method combining the single-layer convolutional features with correlation filter to locate and speed up. Meanwhile accurate scale prediction and high-confidence model update strategy are adopted to solve the scale variation and similarity interfere problems. Extensive experiments on large scale benchmarks demonstrate the effectiveness of the proposed algorithm against state-of-the-art trackers.

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