Visual Tracking By Combining The Structure-Aware Network And Spatial-Temporal Regression

In this paper, we propose a novel visual tracking algorithm by combining the structure-aware network (SA-Net) and spatial-temporal regression model. We first use SA-Net to obtain the initial location proposal, and the deep features are extracted using a fine-tuned convolutional neural network model. Finally, both the location proposal and deep features, including historical information, are input into the long short-term memory (LSTM) for end-to-end spatial temporal regression to adjust the initial location proposal from SA-Net. The experimental results on the challenging OTB dataset demonstrate that the proposed scheme is robust to missing tracking caused by occlusion or object deformation. Additionally, the compared experiments show that the proposed scheme is more competitive than state-of-the-art algorithms.