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
现实场景下拍摄的视频由于存在各种未知失真类型、缺少参考视频,对此类视频的质量评价是一个十分具有挑战性的任务.近年来,研究人员将人类视觉系统的先验知识融合在质量评价任务中.在此基础上,提出一种考虑背景失真的无参考视频质量评...现实场景下拍摄的视频由于存在各种未知失真类型、缺少参考视频,对此类视频的质量评价是一个十分具有挑战性的任务.近年来,研究人员将人类视觉系统的先验知识融合在质量评价任务中.在此基础上,提出一种考虑背景失真的无参考视频质量评价方法.该方法在考虑视频内容的同时,显著增强了对视频背景中信息丢失问题的敏感度,在特征提取阶段充分考虑背景特征的提取;随后,通过引入结合门控机制的通道挖掘技术,高效整合高低维特征,使特征通道更加精准地聚焦于背景失真细节;最终,利用时序建模模块构建特征的时间维度模型,并通过线性回归方法生成视频质量的客观量化评分.使用SROCC(spearman rank order correlation coefficient)、PLCC(pearson linear correlation coefficient)和RMSE(root mean squared error)等评价指标在公开数据集KoNViD-1k、LIVE-Qualcomm和CVD2014开展实验,结果表明该方法不仅与人类主观感知具有高度相关性,且预测误差较小,有效提升了视频质量评估的准确性和可靠性,能够更贴近地模拟人类对视频质量的直观评价.展开更多
The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are...The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.展开更多
For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background mod...For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.展开更多
For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the backgrou...For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.展开更多
A schlieren detection algorithm is proposed for the ground-to-air background oriented schlieren(BOS) system to achieve high-speed airplane shock waves visualization. The proposed method consists of three steps. Firstl...A schlieren detection algorithm is proposed for the ground-to-air background oriented schlieren(BOS) system to achieve high-speed airplane shock waves visualization. The proposed method consists of three steps. Firstly, image registration is incorporated for reducing errors caused by the camera motion.Then, the background subtraction dual-model single Gaussian model(BS-DSGM) is proposed to build a precise background model. The BS-DSGM could prevent the background model from being contaminated by the shock waves. Finally, the twodimensional orthogonal discrete wavelet transformation is used to extract schlieren information and averaging schlieren data. Experimental results show our proposed algorithm is able to detect the aircraft in-flight and to extract the schlieren information. The precision of schlieren detection algorithm is 0.96. Three image quality evaluation indices are chosen for quantitative analysis of the shock waves visualization. The white Gaussian noise is added in the frames to validate the robustness of the proposed algorithm.Moreover, we adopt two times and four times down sampling to simulate different imaging distances for revealing how the imaging distance affects the schlieren information in the BOS system.展开更多
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.
文摘现实场景下拍摄的视频由于存在各种未知失真类型、缺少参考视频,对此类视频的质量评价是一个十分具有挑战性的任务.近年来,研究人员将人类视觉系统的先验知识融合在质量评价任务中.在此基础上,提出一种考虑背景失真的无参考视频质量评价方法.该方法在考虑视频内容的同时,显著增强了对视频背景中信息丢失问题的敏感度,在特征提取阶段充分考虑背景特征的提取;随后,通过引入结合门控机制的通道挖掘技术,高效整合高低维特征,使特征通道更加精准地聚焦于背景失真细节;最终,利用时序建模模块构建特征的时间维度模型,并通过线性回归方法生成视频质量的客观量化评分.使用SROCC(spearman rank order correlation coefficient)、PLCC(pearson linear correlation coefficient)和RMSE(root mean squared error)等评价指标在公开数据集KoNViD-1k、LIVE-Qualcomm和CVD2014开展实验,结果表明该方法不仅与人类主观感知具有高度相关性,且预测误差较小,有效提升了视频质量评估的准确性和可靠性,能够更贴近地模拟人类对视频质量的直观评价.
基金the Doctorate Foundation of the Engineering College, Air Force Engineering University.
文摘The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.
基金Project(60772080) supported by the National Natural Science Foundation of ChinaProject(3240120) supported by Tianjin Subway Safety System, Honeywell Limited, China
文摘For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.
基金supported by the National Natural Science Foundation of China (71871106)the Blue and Green Project in Jiangsu Provincethe Six Talent Peaks Project in Jiangsu Province (2016-JY-011)
文摘For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.
文摘A schlieren detection algorithm is proposed for the ground-to-air background oriented schlieren(BOS) system to achieve high-speed airplane shock waves visualization. The proposed method consists of three steps. Firstly, image registration is incorporated for reducing errors caused by the camera motion.Then, the background subtraction dual-model single Gaussian model(BS-DSGM) is proposed to build a precise background model. The BS-DSGM could prevent the background model from being contaminated by the shock waves. Finally, the twodimensional orthogonal discrete wavelet transformation is used to extract schlieren information and averaging schlieren data. Experimental results show our proposed algorithm is able to detect the aircraft in-flight and to extract the schlieren information. The precision of schlieren detection algorithm is 0.96. Three image quality evaluation indices are chosen for quantitative analysis of the shock waves visualization. The white Gaussian noise is added in the frames to validate the robustness of the proposed algorithm.Moreover, we adopt two times and four times down sampling to simulate different imaging distances for revealing how the imaging distance affects the schlieren information in the BOS system.
