Transfer Learning Approach for Robust Motion Detection with EBM

Motion detection is a fundamental task in computer vision, with applications spanning video surveillance, autonomous navigation, and human-computer interaction. This article introduces a novel motion detection framework that leverages transfer learning and energy-based models (EBM) to address key challenges such as noise, lighting variations, and complex motion patterns. By utilizing pre-trained deep neural networks for feature extraction, the proposed method reduces the need for large-scale labeled datasets while maintaining high robustness across diverse environments. A unified energy computation mechanism integrates spatial and temporal information, enabling accurate detection of motion trajectories with minimal computational overhead. Experimental results demonstrate the effectiveness of the proposed approach under varying conditions, including high noise levels and dynamic scenes, achieving a balanced performance in terms of precision, recall, and computational efficiency.