视频前背景分离的主要目的是从视频中提取感兴趣目标,但是由于噪声、光照变化等的影响使其仍是计算机视觉等领域最具有挑战性的任务之一。截断核范数(truncated nuclear norm,TNN)算法是一种经典的鲁棒主成分分析(robust principal comp...视频前背景分离的主要目的是从视频中提取感兴趣目标,但是由于噪声、光照变化等的影响使其仍是计算机视觉等领域最具有挑战性的任务之一。截断核范数(truncated nuclear norm,TNN)算法是一种经典的鲁棒主成分分析(robust principal component analysis,RPCA)算法,被广泛地应用于视频前背景分离。但是,该算法中的截断核范数对传统鲁棒主成分分析中的秩函数逼近度不高,导致其稳定性不强,对一些复杂场景下的视频前背景分离精度不高。针对该问题,本文提出了一种改进的截断核范数(improved truncated nuclear norm,ITNN)算法。该算法首先采用非凸γ范数替代TNN模型中的核范数,并分析了相对于核范数而言,非凸γ范数对秩函数具有更高的逼近度,同时提出了该算法所对应的模型;其次,为了求解提出的模型,本文引入了广义交替方向乘子法(generalized alternating direction method of multipliers,GADMM)对该模型进行求解;最后,将提出的ITNN算法应用于多个公共视频的前背景分离实验中,并通过展示提取不同视频的前景效果,从视觉角度验证了ITNN算法的有效性。同时,计算提出算法和对比算法提取的视频前景的F-measure值,从量化的角度进一步验证了ITNN算法的有效性。另外,实验还记录了各算法的视频前背景分离的运行时间,验证了ITNN算法的效率。总之,本文通过实验验证了提出的ITNN算法在视频前背景分离中的有效性和优越性。展开更多
低秩稀疏分解(Low Rank and Sparse Decomposition,LRSD)是一种被广泛应用于计算机视觉等领域的数据表示技术,通过将已知矩阵分解为低秩成分和稀疏成分,实现视频前背景分离、图像去噪等的实际应用。分析了这一技术的研究现状,针对11种...低秩稀疏分解(Low Rank and Sparse Decomposition,LRSD)是一种被广泛应用于计算机视觉等领域的数据表示技术,通过将已知矩阵分解为低秩成分和稀疏成分,实现视频前背景分离、图像去噪等的实际应用。分析了这一技术的研究现状,针对11种经典低秩稀疏分解方法,给出了各种方法的模型及算法的优缺点。将各种算法应用于视频前背景分离和图像去噪实验中,视频前背景分离的实验结果包括使用各种算法提取的不同视频的前景效果图、视频前背景分离的F-measure值和运行时间,图像去噪实验结果展示了各种算法对不同图像的去噪效果图、PSNR值和FSIM值,从视觉效果和定量评价两个角度验证了各种算法在视频前背景分离和图像去噪这两个实际应用中的优缺点。展开更多
In this paper, we propose a novel framework to encrypt surveillance videos. Although a few encryption schemes have been proposed in the literature, they are not sufficiently efficient due to the lack of full considera...In this paper, we propose a novel framework to encrypt surveillance videos. Although a few encryption schemes have been proposed in the literature, they are not sufficiently efficient due to the lack of full consideration of the characteristics of surveillance videos, i.e., intensive global redundancy. By taking advantage of such redundancy, we design a novel method for encrypting such videos. We first train a background dictionary based on several frame observations. Then every single frame is parsed into the background and foreground components. Separation is the key to improve the efficiency of the proposed technique, since encryption is only carried out in the foreground, while the background is skillfully recorded by corresponding background recovery coefficients. Experimental results demonstrate that, compared to the state of the art, the proposed method is robust to known cryptanalytic attacks, and enhances the overall security due to the foreground and background separation. Additionally, our encryption method is faster than competing methods, which do not conduct foreground extraction.展开更多
文摘视频前背景分离的主要目的是从视频中提取感兴趣目标,但是由于噪声、光照变化等的影响使其仍是计算机视觉等领域最具有挑战性的任务之一。截断核范数(truncated nuclear norm,TNN)算法是一种经典的鲁棒主成分分析(robust principal component analysis,RPCA)算法,被广泛地应用于视频前背景分离。但是,该算法中的截断核范数对传统鲁棒主成分分析中的秩函数逼近度不高,导致其稳定性不强,对一些复杂场景下的视频前背景分离精度不高。针对该问题,本文提出了一种改进的截断核范数(improved truncated nuclear norm,ITNN)算法。该算法首先采用非凸γ范数替代TNN模型中的核范数,并分析了相对于核范数而言,非凸γ范数对秩函数具有更高的逼近度,同时提出了该算法所对应的模型;其次,为了求解提出的模型,本文引入了广义交替方向乘子法(generalized alternating direction method of multipliers,GADMM)对该模型进行求解;最后,将提出的ITNN算法应用于多个公共视频的前背景分离实验中,并通过展示提取不同视频的前景效果,从视觉角度验证了ITNN算法的有效性。同时,计算提出算法和对比算法提取的视频前景的F-measure值,从量化的角度进一步验证了ITNN算法的有效性。另外,实验还记录了各算法的视频前背景分离的运行时间,验证了ITNN算法的效率。总之,本文通过实验验证了提出的ITNN算法在视频前背景分离中的有效性和优越性。
文摘低秩稀疏分解(Low Rank and Sparse Decomposition,LRSD)是一种被广泛应用于计算机视觉等领域的数据表示技术,通过将已知矩阵分解为低秩成分和稀疏成分,实现视频前背景分离、图像去噪等的实际应用。分析了这一技术的研究现状,针对11种经典低秩稀疏分解方法,给出了各种方法的模型及算法的优缺点。将各种算法应用于视频前背景分离和图像去噪实验中,视频前背景分离的实验结果包括使用各种算法提取的不同视频的前景效果图、视频前背景分离的F-measure值和运行时间,图像去噪实验结果展示了各种算法对不同图像的去噪效果图、PSNR值和FSIM值,从视觉效果和定量评价两个角度验证了各种算法在视频前背景分离和图像去噪这两个实际应用中的优缺点。
基金Acknowledgements This work was supported by National High-tech R＆D Program of China （2013AA01A601 ） and the National Natural Science Foundation of China （Grant No. 61332012）.
文摘In this paper, we propose a novel framework to encrypt surveillance videos. Although a few encryption schemes have been proposed in the literature, they are not sufficiently efficient due to the lack of full consideration of the characteristics of surveillance videos, i.e., intensive global redundancy. By taking advantage of such redundancy, we design a novel method for encrypting such videos. We first train a background dictionary based on several frame observations. Then every single frame is parsed into the background and foreground components. Separation is the key to improve the efficiency of the proposed technique, since encryption is only carried out in the foreground, while the background is skillfully recorded by corresponding background recovery coefficients. Experimental results demonstrate that, compared to the state of the art, the proposed method is robust to known cryptanalytic attacks, and enhances the overall security due to the foreground and background separation. Additionally, our encryption method is faster than competing methods, which do not conduct foreground extraction.
