General Purpose Graphic Processing Units Based Real Time Video Tracking System
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General Purpose Graphic Processing Units Based Real Time Video Tracking System

Authors: Mallikarjuna Rao Gundavarapu, Ch. Mallikarjuna Rao, K. Anuradha Bai

Abstract:

Real Time Video Tracking is a challenging task for computing professionals. The performance of video tracking techniques is greatly affected by background detection and elimination process. Local regions of the image frame contain vital information of background and foreground. However, pixel-level processing of local regions consumes a good amount of computational time and memory space by traditional approaches. In our approach we have explored the concurrent computational ability of General Purpose Graphic Processing Units (GPGPU) to address this problem. The Gaussian Mixture Model (GMM) with adaptive weighted kernels is used for detecting the background. The weights of the kernel are influenced by local regions and are updated by inter-frame variations of these corresponding regions. The proposed system has been tested with GPU devices such as GeForce GTX 280, GeForce GTX 280 and Quadro K2000. The results are encouraging with maximum speed up 10X compared to sequential approach.

Keywords: Connected components, Embrace threads, Local weighted kernel, Structuring element.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123829

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[1] Amazon Elastic Cloud Compute. http://aws.amazon.com/documentation/ June 2009.
[2] Android open source project: Designing for performance. http://developer.android.com/guide/practices/design/performance. Html, April 2009.
[3] H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool. Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346–359, June 2008.
[4] D. Jacquet, F. Hasbani, P. Flatresse, R. Wilson, F. Arnaud, G. Cesana, T. Di Gilio, C. Lecocq, et al, "A 3 GHz Dual Core Processor ARM Cortex TM -A9 in 28 nm UTBB FD-SOI CMOS with Ultra-Wide Voltage Range and Energy Efficiency Optimization", IEEE Journal of Solid-State Circuits, vol. 49, no. 4, pp. 812-826, April 2014.
[5] D. B. Kirk and W. mei W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach (Applications of GPU Computing Series). Morgan Kaufmann, 2010.
[6] Rymut B, Kwolek B. Real-time multiview human pose tracking using GPU-accelerated particle swarm optimization, Concurrency and Computation: Practice and Experience, 2014. DOI: 10.1002/cpe.3329.
[7] Sinha, S.N., Frahm J.-M., Pollefeys M., and Genc Y. Feature tracking and matching in video using programmable graphics hardware. Machine Vision and Applications (MVA), 2007
[8] W. Niu, L. Jiao, D. Han, and Y. Wang. Real-time multi-person tracking in video surveillance. Proceedings of the Pacific Rim Multimedia Conference, 2:1144-1148, 2003.
[9] I. Haritaoglu, D. Harwood, and L. S. Davis. W4: Real-time surveillance of people and their activities. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22:809-830, 2000.
[10] S. Hongeng, R. Nevatia, and F. Bremond. Video-based event recognition: activity representation and probabilistic recognition methods. Computer Vision I mage Understanding, 96(2):129-162, 2004.
[11] Nghiem A., Bremond F., Thonnat M., Ma R. New evaluation approach for video processing algorithms. Proceedings of the IEEE Workshop on Motion and Video Computing (WMVC07); Austin, Texas, USA. February 23–24 2007.
[12] W. Hu, T. Tan, L. Wang, and S. Maybank. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems. Man and Cybernetics, 34(3):334-352, August 2004.
[13] Regazzoni C. S., Visvanathan R., Foresti G. L. Scanning the issue / technology - Special Issue on Video Communications, processing and understanding for third generation surveillance systems. Proceedings of the IEEE. 2001 Oct; 89: 1355–1367
[14] P. Kumar, S. Ranganath, Huang Weimin, and K. Sengupta. Framework for real-time behavior interpretation from traffic video. IEEE Transactions on Intelligent Transportation Systems, 6(1):43-53, 2005.
[15] Tracking and counting moving people. Proceedings of The Second IEEE International Conference on Image Processing, pages 212-216, 1994.
[16] S. An, W. Liu, and S. Venkatesh. Face recognition using kernel ridge regression. pages 1-7, 2007.
[17] F. Lv, X. Song, B. Wu, V. K. Singh, and R. Nevatia. Left-luggage detection using Bayesian inference. 9th International Workshop on Performance Evaluation in Tracking and Surveillance (PETS-CVPR'06), pages 83-90, 2006.
[18] M. Gschwind. The Cell broadband engine: Exploiting multiple levels of parallelism in a chip multiprocessor. International Journal of Parallel Programming, 35(3):233-262, 2007.
[19] S. Williams, J. Shalf, L. Oliker, S. Kamil. P. Husbands, and K. A. Yelick. Scientific computing kernels on the Cell processor. Int. J. Parallel Programming. 35:263-298. 2007.
[20] M. Hidemasa, D. Munehiro, N. Hiroki, and M. Yumi. Multilevel parallelization on the Cell/B.E. for a motion JPEG 2000 encoding server. Proc. I 5th International Conference Multimedia, pages 942-951, 2007
[21] L. Liu, S. Kesavarapu, J. Connell, A. Jagmohan, A. Leem, L. Paulovicks, B. Sheinin, V. L. Tang, and H. Yeo. Video analysis and compression on the STI Cell broadband engine processor. IEEE International Conference on Multimedia and Expo, 2006.
[22] K. Fatahalian, T. J. Knight, M. Houston, M. Erez, D. R. Horn, L. Leem, J-Y. Park, M. Ren, A. Aiken, W. J. Daily, and P. Hanrahan. Sequoia: Programming the memory hierarchy, p. online. Proc. ACM/IEEE Conference Supercomputing, 2006.
[23] B. Bouzas, R. Cooper, J. Greene, M. Pepe, and M-J. Prelle. Multicore framework: An API for programming heterogeneous multicore processors. Technical report, Mercury Computer Systems, Inc., 2006.
[24] Fan, Z., Qiu, F., Kaufman, A., and Yoakum-Stover, S.GPU Cluster for High Performance Computing. in Proc. Of the 2004 ACM/IEEE Conf. on Supercomputing, 2004.
[25] Ohmer, J. F., Maire, F., and Brown, R. 2006. Real-Time Tracking with Non-Rigid Geometric Templates Using the GPU. In Proc. of the Int. Conf. on Computer Graphics, Imaging and Visualisation (July 26 - 28, 2006). CGIV. IEEE Computer Society, Washington, DC, 200-206.
[26] NVIDIA, CUDA Programming Guide Version 2.0. 2008, NVIDIA Corporation: Santa Clara, California.
[27] Li Yao and Miaogen Ling, An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream, The Scientific World Journal Volume 2014 (2014), Article ID 424050.
[28] Intel, Quad-Core Intel® Xeon® Processor 5400 Series.2008, Intel Corporation: Santa Clara, California.