Dual Gaussian mixture model with pixel history for background suppression

Background suppression has found several application areas in computer vision. Among others, abandoned object detection and moving object detection under dynamic backgrounds are two major application areas. Gaussian mixture model (GMM) is one of the most popular methods and has been applied in both of these application areas. However, the aforementioned areas present contradicting challenges for GMM. Abandoned objects need the GMM to slowly get adapted to prevent accidental assimilation of the foreground, while dynamic backgrounds need quick assimilation to prevent noisy detection. A novel dual GMM based on pixel history is proposed to provide practical solutions to both of these contradictory challenges. The method uses separate GMMs to represent foreground and background modes. A foreground mode is transferred to the GMM representing the background based on its history of occurrence. Thus, a quick assimilation is offered to dynamic backgrounds while abandoned objects face a very slow assimilation. In each case, old background is preserved. Extensive experiments are done to demonstrate the effectiveness of the proposed method and to compare it with a number of well known methods in literature.