Infrared spectral imaging-based image recognition for motion detection

The current infrared imaging recognition methods are inadequate for real-time performance and accuracy for moving objects. Furthermore, they are subject to several constraints, which makes it challenging to recognize stationary and occluded objects. Experts have conducted comprehensive research on infrared imaging, including the development of contour-based infrared motion video image acquisition, the introduction of novel infrared image generation models that align with infrared imaging principles, and the formulation of innovative methods for the joint classification of spatial-spectral and hyper-spectral images. However, none of these advancements have been implemented for enhancement. In order to improve the infrared motion target detection technology, research on image recognition technology based on infrared spectral imaging, the establishment of infrared radiation characteristics model converted image, and combined with the local binary mode for motion target feature extraction, the construction of the background model, applied to the motion detection in the recognition of motion targets. The results demonstrated that the combination effect of local binary pattern feature extraction and analysis of feature vectors increased in accuracy and detection rate with the number of images. Compared to other algorithms, the research algorithm demonstrated a superior signal-to-noise ratio and gain amplitude. The unmanned aerial vehicle signal-to-noise ratio was 13.487, with a gain amplitude of 2.214, while the civil aviation aircraft signal-to-noise ratio was 6.369, with a gain amplitude of 1.792. Therefore, using infrared image feature vectors for image recognition is more effective in motion detection, providing valuable insights for improving the recognition and detection performance of infrared detection technology. • Infrared imaging technology advances for target motion detection. •Study proposes motion detection using infrared spectral imaging. •Combined local binary pattern method enhances detection accuracy. •Experimental results show improved correct and detection rates. •Proposed technique offers higher signal-to-noise ratio in image recognition.