Quality-Aware Feature Aggregation Network for Robust RGBT Tracking

• 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
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2020.2980735

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.