Multimodal Pedestrian Detection in Infrared and Visible Imagery
Summary
Pedestrian detection systems benefit significantly from the joint use of infrared (thermal) and visible-light cameras, especially under challenging lighting and environmental conditions. Visible imagery offers rich texture and colour cues, while infrared imagery provides robust thermal signatures that reveal human presence at night, in fog or through partial occlusion. The principal challenge lies in aligning and fusing two complementary streams of data so that a detection model can draw on the strengths of each modality without being confounded by misregistration or redundant information. Modern approaches typically follow a three-stage pipeline: image registration to ensure geometric correspondence; feature extraction, often via deep convolutional neural networks; and multimodal fusion, which may be performed early (pixel or feature concatenation), at an intermediate level (attention-based weighting) or late (ensemble of independent detectors). Recent advances have introduced transformer-based attention mechanisms, mutual information-driven redundancy suppression and specialised loss functions to promote effective cross-modal learning. These methods have demonstrated improved detection accuracy and robustness across benchmark datasets, with applications in autonomous vehicles, surveillance, search and rescue, and intelligent transportation systems.
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Multimodal Pedestrian Detection in Infrared and Visible Imagery publication trend
The graph below shows the total number of articles in multimodal pedestrian detection in infrared and visible imagery across all publications each year (not limited to Nature Index journals).
Technical terms
Multimodal fusion: The process of combining information from two or more sensor modalities to produce richer feature representations.
Convolutional neural network (CNN): A deep learning architecture that applies convolutional filters to extract hierarchical image features.
Transformer: A neural network model employing self-attention mechanisms to capture long-range dependencies in feature sequences.
Cross-modal attention: A mechanism that learns to weight features from one modality based on information from another.
Mutual information minimisation: An objective that reduces redundant information between paired feature sets to enhance complementary learning.
Mean average precision (mAP): A standard metric for object detection that summarises precision–recall performance across object classes.
References
- Robust Pedestrian Detection by Combining Visible and Thermal Infrared Cameras. Sensors (2015).
- Dual-YOLO Architecture from Infrared and Visible Images for Object Detection. Sensors (2023).
- Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy. Remote Sensing (2022).
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