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
A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models ...A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.展开更多
To reveal the seismogenic mechanism of the Luding earthquake, we employed the 118 China Seismic Network stations to collect the P-wave polarity data from each station, which was then used in the P-wave first motion ap...To reveal the seismogenic mechanism of the Luding earthquake, we employed the 118 China Seismic Network stations to collect the P-wave polarity data from each station, which was then used in the P-wave first motion approach to calculate the focal mechanism solution of the M6.8 Luding earthquake that occurred on September 5,2022. We have also studied the loading effect of tectonic stress on the Luding earthquake fault based on the stress field data for the research area. The results indicate that this earthquake was a strike-slip type, the nodal plane I:strike 167°, dip angle 78°, slip angle 2°;Nodal plane II: strike 77°, dip angle 88°, slip angle 168°. The two fault planes’ instability coefficients of the Luding earthquake are examined considering the region’s background stress field’s condition. The nodal plane I in the Moho circle is discovered to practically coincide with the Coulomb failure line and the tangent point of the Moho circle, indicating that this nodal plane has a high instability coefficient compared to the nodal plane II. The conclusion is that the nodal plane I has a higher likelihood of being the seismogenic fault plane, which is congruent with the seismogenic fault plane suggested by the aftershock distribution, the earthquake radiation energy distribution of a single station, and seismic intensity distribution.The Luding earthquake’s focal mechanism is highly like the theoretical focal mechanism of the fault situated at the location where the Coulomb failure line intersects the Mohr circle, demonstrating that background stress is what caused the earthquake. The substantial fault instability and similarity between the solved and theoretical focal mechanisms make it easier to comprehend the loading effect of tectonic stress on the Luding earthquake fault.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.
基金Project(T201221207)supported by the Fundamental Research Fund for the Central Universities,ChinaProject(2012CB725301)supported by National Basic Research and Development Program,China
文摘A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.
基金supported by the Special Found of the Institute of Geophysics, China Earthquake Administration (DQJB22B18)
文摘To reveal the seismogenic mechanism of the Luding earthquake, we employed the 118 China Seismic Network stations to collect the P-wave polarity data from each station, which was then used in the P-wave first motion approach to calculate the focal mechanism solution of the M6.8 Luding earthquake that occurred on September 5,2022. We have also studied the loading effect of tectonic stress on the Luding earthquake fault based on the stress field data for the research area. The results indicate that this earthquake was a strike-slip type, the nodal plane I:strike 167°, dip angle 78°, slip angle 2°;Nodal plane II: strike 77°, dip angle 88°, slip angle 168°. The two fault planes’ instability coefficients of the Luding earthquake are examined considering the region’s background stress field’s condition. The nodal plane I in the Moho circle is discovered to practically coincide with the Coulomb failure line and the tangent point of the Moho circle, indicating that this nodal plane has a high instability coefficient compared to the nodal plane II. The conclusion is that the nodal plane I has a higher likelihood of being the seismogenic fault plane, which is congruent with the seismogenic fault plane suggested by the aftershock distribution, the earthquake radiation energy distribution of a single station, and seismic intensity distribution.The Luding earthquake’s focal mechanism is highly like the theoretical focal mechanism of the fault situated at the location where the Coulomb failure line intersects the Mohr circle, demonstrating that background stress is what caused the earthquake. The substantial fault instability and similarity between the solved and theoretical focal mechanisms make it easier to comprehend the loading effect of tectonic stress on the Luding earthquake fault.
