Advanced Motion Recognition Using High-Resolution Infrared Imaging and Enhanced Image Processing Techniques

Over the past few years, infrared (IR) motion detection has become more important in domains such as security, wildlife monitoring, and nighttime surveillance, when standard vision-based systems frequently fail. The approach of infrared video detection using convolutional neural networks is been implemented in this research work. Infrared imaging is particularly useful for collecting temperature-based data in low-visibility or dark conditions, enabling for the monitoring of activities that would otherwise be difficult to trace. The suggested approach preprocesses grayscale infrared pictures, normalizing them and scaling to fit the input requirements of a custom-built CNN. Through a sequence of convolutional and pooling layers, the CNN collects characteristics related to motion patterns in IR pictures, learning to discriminate between frames having motion and those that do not. The proposed system was developed with the help of pre-trained data values of infrared pictures, and performance was tested using multiple parameters that includes various visualization figures such as accuracy-precision graph, recall, and ROC-AUC, to show its usefulness. This real-time approach combines a CNN with a video processing pipeline to identify motion in an infrared movie. It uses visualizations like as confusion matrices, accuracy-loss curves, and precision-recall curves to give insights into model design, implementation details, and experimental results. The results reveal that CNNs can accurately discern motion in infrared pictures, paving the way for automated low-light surveillance.