Open AccessEnd‐to‐end feature fusion Siamese network for adaptive visual tracking
Author(s) -
Guo Dongyan,
Wang Jun,
Zhao Weixuan,
Cui Ying,
Wang Zhenhua,
Chen Shengyong
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12009
Subject(s) - discriminative model , artificial intelligence , computer science , feature (linguistics) , pattern recognition (psychology) , layer (electronics) , salient , fuse (electrical) , feature extraction , computer vision , fusion , tracking (education) , convolution (computer science) , channel (broadcasting) , artificial neural network , engineering , psychology , pedagogy , philosophy , linguistics , computer network , chemistry , organic chemistry , electrical engineering
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.