RGBT Tracking by Trident Fusion Network

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 2; pp. 579 - 592
• Main Authors: Zhu, Yabin, Li, Chenglong, Tang, Jin, Luo, Bin, Wang, Liang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Benchmark testing; Convolution; feature aggregation; Feature extraction; feature pruning; Machine learning; Optical tracking; Representations; RGBT tracking; Robustness; Target tracking; Training; trident architecture; Visual fields; Visualization

In recent years, RGBT tracking has become a hot topic in the field of visual tracking, and made great progress. In this paper, we propose a novel Trident Fusion Network (TFNet) to achieve effective fusion of different modalities for robust RGBT tracking. In specific, to deploy the complementarity of...