The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help...The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.展开更多
To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction alg...To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.展开更多
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
Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed metho...Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed method to combine color and texture characteristics. Suppression and relaxation are the two key strategies to resist illumination changes and shadow disturbance. The proposed method is quite efficient and is capable of resisting illumination changes. Experimental results show that our method is suitable for real-word scenes and real-time applications.展开更多
文摘The paper first discusses shortcomings of classical adjacent-frame difference. Sec ondly, based on the image energy and high order statistic(HOS) theory, background reconstruction constraints are setup. Under the help of block-processing technology, background is reconstructed quickly. Finally, background difference is used to detect motion regions instead of adjacent frame difference. The DSP based platform tests indicate the background can be recovered losslessly in about one second, and moving regions are not influenced by moving target speeds. The algorithm has important usage both in theory and applications.
文摘To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.
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 Asia University under Grant No.100-ASIA-38
文摘Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed method to combine color and texture characteristics. Suppression and relaxation are the two key strategies to resist illumination changes and shadow disturbance. The proposed method is quite efficient and is capable of resisting illumination changes. Experimental results show that our method is suitable for real-word scenes and real-time applications.
