An Improved Real-Time Noise Removal Method in Video StreamBased on Pipe-and-Filter Architecture

الموضوعات : Journal of Computer & Robotics

Vahid Fazel Asl ¹ (Faculty of Computer and Information Technology Engineering, Qazvin Branch,Islamic Azad University, Qazvin, Iran.)
Babak Karasfi ² (Faculty of Computer and Information Technology Engineering, Qazvin Branch,Islamic Azad University, Qazvin, Iran.)
Behrooz Masoumi ³ (Faculty of Computer and Information Technology Engineering, Qazvin Branch,Islamic Azad University, Qazvin, Iran.)
Mohamadreza Keyvanpor ⁴ (Computer Engineering Department, Alzahra University, Tehran, Iran)

تاريخ الإرسال : 21 السبت , شعبان, 1442 تاريخ التأكيد : 17 الأحد , ذو القعدة, 1442 تاريخ الإصدار : 20 الثلاثاء , شوال, 1442

الکلمات المفتاحية: image processing, background removal, Directshow framework, pipe-and-filter architecture,

ملخص المقالة :

Automated analysis of video scenes requires the separation of moving objects from the background environment, which could not separate moving items from the background in the presence of noise. This paper presents a method to solve this challenge; this method uses the Directshow framework based on the pipe-and-filter architecture. This framework trace in three ways. In the first step, the values of the MSE, SNR, and PSNR criteria calculate. In this step, the results of the error criteria are compared with applying salt and pepper and Gaussian noise to images and then applying median, Gaussian, and Directshow filters. In the second step, the processing time for each method check in case of using median, Gaussian, and Directshow filter, and it will result that the used method in the article has high performance for real-time computing. In the third step, error criteria of foreground image check in the presence or absence of the Directshow filter. In the pipe-and-filter architecture, because filters can work asynchronously; as a result, it can boost the frame rate process, and the Directshow framework based on the pipe-and-filter architecture will remove the existing noise in the video at high speed. The results show that the used method is far superior to existing methods, and the calculated values for the MSE error criteria and the processing time decrease significantly. Using the Directshow, there are high values for the SNR and PSNR criteria, which indicate high-quality image restoration. By removing noise in the images, you could also separate moving objects from the background appropriately.

المصادر:

1. Ji and et al., Robust video denoising using low rank matrix completion. in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1791-1798, 2010.


2. H.R.Schmidtke, A survey on verification strategies for intelligent transportation systems. Journal of Reliable Intelligent Environments,  vol. 4, no. 4, pp. 211–224, 2018.


3. K.Vignesh, G. Yadav and A.Sethi, Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge. Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2161-2168, 2017.


4. Jalalian, B. Karasfi, K. Samsudin, M. I. Saripan and S. Mashohor, Fast Cellular Automata Implementation on Graphic Processor Unit (GPU) for Salt and Pepper Noise Removal. Journal of Computer & Robotics, vol. 7, no. 2, pp. 21–28, 2014.


5. Shah, J. D. Deng and B. J. Woodford, Video background modeling: recent approaches, issues and our proposed techniques. Machine Vision and Applications, vol. 25, no. 5, pp. 1105-1119, 2014.


6. P. Donovan and S. F. Sidney, Evaluation of Background Subtraction Algorithms with Post-Processing. 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, pp. 192-199, 2008.


7. Shimada, Y. Nonaka, H. Nagahara and R. Taniguchi, Case-based background modeling: associative background database towards low-cost and high-performance change detection. Machine Vision and Applications, vol. 25, no. 5, pp. 1121–1131, 2014.


8. Alvar, A. Rodriguez-Calvo, A. Sanchez-Miralles and A. Arranz, Mixture of Merged Gaussian Algorithm using RTDENN. Machine Vision and Applications, vol. 25, no. 5, pp. 1133-1144, 2013.


9. Xu, J. Donga, B. Zhang and D. Xu, Background modeling methods in video analysis: A review and comparative evaluation. CAAI Transactions on Intelligence Technology, vol. 1, no. 1, pp. 43-60, 2016.


10. Goyal and J. Singhai, Review of background subtraction methods using Gaussian mixture model for video surveillance systems. Artificial Intelligence Review, vol. 50, no. 2, pp. 1-19, 2018.


11.  


12. Joudaki, M. S. Bin Sunar and H. Kolivand, Background Subtraction Methods in Video Streams: A Review. 4th International Conference on Interactive Digital Media (ICIDM), pp. 1-6, 2015.


13. ouwmans, Traditional and recent approaches in background modeling for foreground detection: An overview. Computer Science Review, vol. 11, pp. 31-66, 2015.


14. Gruenwedel, N. I. Petrovic, L. Jovanov, J. O. Nino-Casta-neda, A. Pizurica and W. Philips, Efficient foreground detection for real-time surveillance applications. Electronics Letters, vol. 49, no. 18, pp. 1143-1145, 2013.


15. Bouwmans, J. Gonzalez, C. Shan, M. Piccardi and L. Davis, Special issue on background modeling for foreground detection in real-world dynamic scenes. Machine Vision and Applications, vol. 25, no. 5, pp. 1101–1103, 2014.


16. Bayona, J. C. SanMiguel and J. M. Martínez, Stationary foreground detection using background subtraction and temporal difference in video surveillance. Image Processing (ICIP), pp. 4657-4660, 2010.


17. Valera and S. A. Velastin, Intelligent distributed surveillance systems: a review. IEE Proceedings - Vision, Image and Signal Processing, vol. 152, no. 2, pp. 192-204, May 2005.


18. Babcock, S. Babu, M. Datar, R. Motwani and J. Widom, Models and issues in data stream systems. Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 1-16, June 2002.


19. Linetsky, Programming Microsoft Directshow. 2001.


20. Zivkovic, Improved adaptive Gaussian mixture model for background subtraction. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 2, pp. 28-31, 2004.


21. Wojnarski, Debellor: A Data Mining Platform with Stream Architecture. Springer Berlin Heidelberg, vol. 9, pp. 405-427, 2008.


22. Rybok, S. Friedberger, U. D. Hanebeck and R. Stiefelhagen, The KIT Robo-kitchen data set for the evaluation of view-based activity recognition systems. 2011 11th IEEE-RAS International Conference on Humanoid Robots, pp. 128-133, 2011.