Unsupervised motion detection with background update and shadow suppression

An algorithm is developed to detect moving object and suppress shadow. According to motion variations caused by some moving objects in a scene, a background update approach is proposed. The developed update method efficiently prevents undesired corruption of background and does not consider the adaptation coefficient or the learning rate used in some existing algorithms. A multi-scale wavelet transform methodology is used to segment foreground from a clutter background. The optimal selection of threshold value is automatically determined which does not require any complex supervised training or manual calibration. According to photometric invariant, a color ratio difference is proposed to suppress shadow. Some complete foreground motion object regions are extracted by integrating moving object segmentation in the multi-scale wavelet with shadow suppression in the color ratio difference. The mentioned method is less affected by the presence of moving objects in a scene. Experimental results show that the proposed approach is efficient in detecting motion objects and suppressing shadows by comparisons.