Robust visual tracking based on spatial context pyramid

In recent years, discriminative correlation filter (DCF) has gained a lot of popularity in visual tracking, mainly due to its circular sampling from limited training data and computational efficiency in Fourier domain. However, such trackers do not make reasonable use of context information, resulting in limited performance. In this paper, we propose a novel DCF tracking framework based on spatial context pyramid (SCPT) to overcome this problem. Firstly, we take global spatial context into account to exploit the relationship between the target and its context for better tracking. Secondly, we design an effective spatial window to highlight the target while suppressing the background, and thus a robust filter model which has a high response for the target and low response for the background can be learned. Thirdly, we construct a context pyramid representation using multi-level spatial windows for adapting different challenging factors. To validate the compatibility of the proposed algorithm, we implement two versions with the representations from both conventional features and deep convolutional neural network (CNN) features. Extensive experimental results on the OTB-2013 benchmark demonstrate the effectiveness of the proposed tracker in terms of accuracy and robustness.