<p>This study presents an intensity-based framework for motion analysis in near-infrared (NIR) video, designed to differentiate motion types and gain insights into their characteristics through a comparative evaluation of signal extraction techniques. Leveraging the Hilbert and Fourier Transforms, the framework captures intensity variations across video frames to analyze distinct motion patterns. The proposed framework is versatile and applicable to any type of motion; however, in this research, it is specifically applied to human motion due to its inherent complexity. Five signal extraction methods are applied: Summed Absolute Differences Mean, Central Axis Difference Mean, Gradient Difference Mean, Variance of Difference Means, and Average Difference Mean. Each technique provides unique insights into motion dynamics by isolating horizontal and vertical intensity shifts. The Hilbert Transform extracts envelope, instantaneous phase, and frequency information for stability assessment, while the Fourier Transform identifies cadence and periodicity. Results indicate that this intensity-based approach, combined with advanced signal analysis, offers a robust and computationally efficient alternative to conventional computer vision techniques, making it particularly effective for low-light motion analysis.</p>