Self-Adaptive Gaussian Mixture Model for Urban Traffic Monitoring System

Identifying moving vehicles is a critical task for an urban traffic monitoring system. With static cameras, background subtraction techniques are commonly used to separate foreground moving objects from background at the pixel level. Gaussian mixture model is commonly used for background modelling. Most background modelling techniques use a single leaning rate of adaptation which is inadequate for complex scenes as the background model cannot deal with sudden illumination changes. In this paper, we propose a self-adaptive Gaussian mixture model to address these problems. We introduce an online dynamical learning rate and global illumination of background model adaptation to deal with fast changing scene illumination. Results of experiments using manually-annotated urban traffic video with sudden illumination changes illustrate that our algorithm achieves consistently better performance in terms of ROC curve, detection accuracy, Matthews correction coefficient and Jaccard coefficient compared with other approaches based on the widely-used Gaussian mixture model.