Research on Computer Vision Methods for Human Identification in Low Visibility Conditions
DOI:
https://doi.org/10.31861/sisiot2026.1.01003Keywords:
computer vision, re-identification, visible-infrared spectrum, image fusion, deep learningAbstract
The article discusses the pressing scientific and practical problem of dependable human detection and identification in situations of low visibility, including dense fog, thick smoke, or extremely inadequate lighting. The research offers a systematic analysis of hybrid approaches based on the complementary integration of data from both the visible and infrared spectra. It is suggested that by synergizing these modalities, it may be possible to overcome the fundamental limitations of mono-spectral sensors, supporting robust performance in environments where traditional optical or thermal cameras individually fail. The article synthesizes a conceptual architectural model for a two-stream neural network specifically for unmanned aerial vehicles based on a comprehensive analysis and systematization of current sensor fusion methods. Particular focus is being placed on the theoretical development of a pre-assessment module logic. This module can dynamically switch between processing modes based on real-time image entropy analysis to make the best use of computing power. The research identifies a suitable technological stack, which includes end-to-end restoration architectures and lightweight integration blocks, such as temperature-guided lightweight fusion, adapted for the hardware constraints of embedded systems. The analysis indicates potential benefits of this hybrid approach in reducing spectral pollution and supporting adherence to data privacy regulations, particularly the General Data Protection Regulation, compared with mono-spectral alternatives. The article concludes by outlining strategic opportunities for the advancement of autonomous search and rescue systems, with a particular emphasis on the potential of federated learning principles for secure and decentralized model updates in practical settings.
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