Back to Search Start Over

Adaptive Fusion CNN Features for RGBT Object Tracking.

Authors :
Wang, Yong
Wei, Xian
Tang, Xuan
Shen, Hao
Zhang, Huanlong
Source :
IEEE Transactions on Intelligent Transportation Systems; Jul2022, Vol. 23 Issue 7, p7831-7840, 10p
Publication Year :
2022

Abstract

Thermal sensors play an important role in intelligent transportation system. This paper studies the problem of RGB and thermal (RGBT) tracking in challenging situations by leveraging multimodal data. A RGBT object tracking method is proposed in correlation filter tracking framework based on short term historical information. Given the initial object bounding box, hierarchical convolutional neural network (CNN) is employed to extract features. The target is tracked for RGB and thermal modalities separately. Then the backward tracking is implemented in the two modalities. The difference between each pair is computed, which is an indicator of the tracking quality in each modality. Considering the temporal continuity of sequence frames, we also incorporate the history data into the weights computation to achieve a robust fusion of different source data. Experiments on three RGBT datasets show the proposed method achieves comparable results to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15249050
Volume :
23
Issue :
7
Database :
Complementary Index
Journal :
IEEE Transactions on Intelligent Transportation Systems
Publication Type :
Academic Journal
Accession number :
157955753
Full Text :
https://doi.org/10.1109/TITS.2021.3073046