Shadow Elimination Based on Improved Vibe and Multiple Features Fusion

Shadow removal is one of the most important parts of moving object detection in the field of intelligent video surveillance since the shadow definitely affects the recognition result. There is a flaw when ViBe algorithm is used to detect moving vehicles, that is, the ghost of moving vehicles would exist over a period in vehicles detection if moving vehicles exist in the first frame and interfere the following detection effect. Chromaticity-based approach are susceptible to noise, furthermore it is sensitive to illumination changes. Texture-based approach is highly distinctive, do not depend on colors and robust to illumination changes. In terms of the disadvantage of above approaches, a new improved ViBe algorithm combined with multiple features fusion, fusing chromaticity and texture is proposed in this paper. The initialization model would be initialized by Gaussian distribution probability function and updated according to original ViBe update algorithm in the proposed approach, next fusing the chromaticity feature and texture feature to remove the shadow. Experimental results show the proposed approach can detect the moving vehicles as well as remove the ghost effectively, and it is efficient and robust in shadow removing under different scenes.