Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from ...Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.展开更多
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 ef-...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 ef- ficiently 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 clut- ter 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 sup- press 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.展开更多
文摘Segmentation of moving objects efficiently from video sequence is very important for many applications. Background subtraction is a common method typically used to segment moving objects in image sequences taken from a statistic camera. Some existing algorithms cannot adapt to changing circumstances and require manual calibration in terms of specification of parameters or some hypotheses for changing background. An adaptive motion segmentation method is developed according to motion variation and chromatic characteristics, which prevents undesired corruption of the background model and does not consider the adaptation coefficient. RGB color space is selected instead of introducing complex color models to segment moving objects and suppress shadows. A color ratio for 4-connected neighbors of a pixel and multi-scale wavelet transformation are combined to suppress shadows. The mentioned approach is scene-independent and high correct segmentation. It has been shown that the approach is robust and efficient to detect moving objects by experiments.
基金supported by the National Natural Science Foundation of China(Nos.11176016,60872117)
文摘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 ef- ficiently 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 clut- ter 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 sup- press 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.
