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A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance

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A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance Abstract—Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multiscenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multiscenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multiscenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than do other state-of the- art methods. To Get the project in online or offline contact us 9566355386 / 9962588976
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   Any Query Call Us: 9566355386 A Robust Moving Object Detection in Multi-Scenario Big Data for Video Surveillance Abstract  —  Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multiscenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multiscenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multiscenario videos. Quantitative and qualitative assessments indicate that the  proposed model outperforms existing methods and achieves substantially more robust performance than do other state-of the- art methods. CONCLUSIONS In this paper, we presented novel sparse and low-rank representation technology within a contextual regularization model for motion detection. Foreground and  background models were carefully considered in the development of the proposed model; thus, the model can accurately decompose a multiscenario video sequence into background and foreground to improve the performance of single scenario- based moving object detection. The proposed model can suppress false positives from the background and preserve fine foreground pixels. Hence, the proposed method yields foreground masks with a greater accuracy than that of current state-of-the-art methods. Quantitative and qualitative assessments demonstrated that the   Any Query Call Us: 9566355386  proposed method outperforms these state-of-the-art methods in application to multiscenario video sequences. SYSTEM REQUIREMENTS:   HARDWARE REQUIREMENTS: ã   System : Pentium IV 2.4 GHz. ã   Hard Disk : 40 GB. ã   Floppy Drive : 1.44 Mb. ã   Monitor : 15 VGA Colour. ã   Mouse : Logitech. ã   Ram : 512 Mb   SOFTWARE REQUIREMENTS:      Operating system : Windows 7/UBUNTU.    Coding Language : Java 1.7 ,Hadoop 0.8.1    IDE : Eclipse    Database : MYSQL REFERENCES [1] T. Bouwmans, A. Sobral, S. Javed, S. K. Jung, and E.-H. Zahzah, ―Decomposition into low -rank plus additive matrices for background/- foreground separation: A review for a comparative evaluation with a large- scale dataset,‖ Computer Science Review, vol. 23, pp. 1  –   71, 2017. [2] B. H. Chen and S. C. Huang, ―An advanced moving object detection  algorithm for automatic traffic monitoring in real-world limited bandwidth networks,‖ IEEE Transactions on Multimedia, vol. 16, no. 3, pp. 837  –  847, April 2014.   Any Query Call Us: 9566355386 [3] L. Maddalena and A. Petrosino, ―A self  -organizing approach to background subtraction for visual surveillance applications,‖ IEEE Transactions  on Image Processing, vol. 17, no. 7, pp. 1168  –  1177, July 2008.
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