FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking
This addresses robust tracking for applications in challenging environments like low illumination or occlusion, but it is incremental as it builds on existing RGBT tracking methods.
The paper tackles robust visual tracking in adverse conditions using RGB and thermal data by proposing FANet, a quality-aware feature aggregation network, which achieves high-accuracy performance on large-scale benchmarks compared to state-of-the-art methods.
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 qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation, low illumination, 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 1x1 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.