End‐to‐end feature fusion Siamese network for adaptive visual tracking

• Main Authors: Dongyan Guo, Jun Wang, Weixuan Zhao, Ying Cui, Zhenhua Wang, Shengyong Chen
• Format: Article
• Language: English
• Published: Wiley 2021-01-01
• Series: IET Image Processing
• Online Access: https://doi.org/10.1049/ipr2.12009

Abstract According to observations, different visual objects have different salient features in different scenarios. Even for the same object, its salient shape and appearance features may change greatly from time to time in a long‐term tracking task. Motivated by them, an end‐to‐end feature fusion framework was proposed based on the Siamese network, named FF‐Siam, which can effectively fuse different features for adaptive visual tracking. The framework consists of four layers. A feature extraction layer is designed to extract the different features of the target region and search region. The extracted features are then put into a weight generation layer to obtain the channel weights, which indicate the importance of different feature channels. Both features and the channel weights are utilised in a template generation layer to generate a discriminative template. Finally, the corresponding response maps created by the convolution of the search region features and the template are applied with a fusion layer to obtain the final response map for locating the target. Experimental results demonstrate that the proposed framework achieves state‐of‐the‐art performance on the popular Temple‐Colour, OTB50 and UAV123 benchmarks.