Quality-Aware Feature Aggregation Network for Robust RGBT Tracking

RGBT tracking becomes a popular computer vision task, and has a variety of applications in visual surveillance systems, self-driving cars and intelligent transportation system. This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary vis...

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Bibliographic Details
Published in:IEEE transactions on intelligent vehicles 2021-03, Vol.6 (1), p.121-130
Main Authors: Zhu, Yabin, Li, Chenglong, Tang, Jin, Luo, Bin
Format: Article
Language:English
Subjects:
Agglomeration
Autonomous cars
Clutter
Computer architecture
Computer vision
Convolution
feature aggregation
Feature extraction
Infrared tracking
Intelligent transportation systems
Network architecture
Occlusion
Optical tracking
quality-aware fusion
Reliability
RGBT tracking
Robustness
Surveillance systems
Target tracking
Task analysis
Transportation networks
Visualization
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Summary:RGBT tracking becomes a popular computer vision task, and has a variety of applications in visual surveillance systems, self-driving cars and intelligent transportation system. This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called quality-aware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to dispose the challenge of significant changes in appearance which is triggered by low illumination,deformation, background clutter and occlusion. In particular, we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution, and use 1×1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation. To model the interactions between RGB and thermal modalities, we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources. The whole FANet is trained in an end-to-end manner. Extensive experiments on large-scale benchmark datasets demonstrate the high-accurate performance against other state-of-the-art RGBT tracking methods.
ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2020.2980735