One-Shot Scale and Angle Estimation for Fast Visual Object Tracking

Visual object tracking is the process of tracking an arbitrary object in a video, where the bounding box of the object is given in the first frame. Siamese network-based visual object tracking approaches have recently received considerable attention for their high speed and superior performance. However, for scale and angle estimation, Siamese trackers require multiple search regions, which increases the computation time, thereby decreasing the real-time tracking performance. This paper proposes a one-shot Siamese network, named Siam-OS, for fast and efficient visual object tracking. Siam-OS uses only a single search region and estimates the scale and angle of the target bounding box. This significantly reduces the number of computations required for the deep convolutional feature extraction, and thus increases the tracking speed. The experimental results with Visual Object Tracking (VOT) benchmarks show the effectiveness of the proposed Siam-OS in terms of the accuracy, robustness, expected average overlap, and speed.