Moving target detection based on improved Gaussian mixture model in dynamic and complex environments

Abstract

Recently, background modeling has garnered significant attention for motion target detection in vision and image applications. However, most methods do not achieve satisfactory results because of the influence of background dynamics and other factors. The Gaussian mixture model (GMM) background modeling method is a popular and powerful motion background modeling technology owing to its ability to balance robustness and real-time constraints in various practical environments. However, when the background is complex and the target moves slowly, the traditional GMM cannot accurately detect the target and is prone to misjudging the moving background as a moving target. To address the interference from complex backgrounds, this study proposes a target detection method that combines an adaptive GMM with an improved three-frame difference method, along with an algorithm that combines grayscale statistics with an improved Phong illumination model for illumination compensation and shadow removal. The experimental results demonstrate that the improved method has better robustness, improves target detection accuracy, and reduces noise and background interference.