CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

Authors

  • Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
  • Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19651

Keywords:

Parallel computing, Visual surveillance, Graphics processing unit, Multi-core

Abstract

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object tracking
during a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units.

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References

Hu W, Tan T, Wang L, Maybank S. A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern C 2004;34:334-52.

Velastin SA, Remagnino P. Intelligent Distributed Video Surveillance Systems. London, UK: IET Digital Library; 2006.

Collins RT, Lipton AJ, Kanade T. Introduction to the special section on video surveillance. IEEE Trans Pattern Anal Mach Intell 2000;22:745-6.

Howarth RJ, Buxton H. Conceptual descriptions from monitoring and watching image sequences. Image Vis Comput 2000;18(2):105-35.

Hu W, Xie D, Tan T. A hierarchical self-organizing approach for learning the patterns of motion trajectories. IEEE Trans Neural Netw 2004;15(1):135-44.

Tian Y, Tan TN, Sun HZ. A novel robust algorithm for real-time object tracking. Acta Automat Sin 2002;28:851-3.

Wu Y, Liu Q, Huang TS. An Adaptive Self-Organizing Color Segmentation Algorithm with Application to Robust Real-Time Human Hand Localization. In: Proceedings of 4th Asian Conference on Computer Vision, Taipei, Taiwan, 8-11, January; 2000. p. 1106-11.

Howarth RJ, Buxton H. Analogical representation of space and time. Image Vis Comput 1992;10(7):467-78.

Brand M, Kettnaker V. Discovery and segmentation of activities in video. IEEE Trans Pattern Anal Mach Intell 2000;22(8):844-51.

Garcia-Rodriguez J, Garcia-Chamizo JM. Surveillance and human-computer interaction applications of self-growing models. Appl Soft Comput 2011;11(7):4413-43.

Nageswaran JM, Dutt N, Krichmar JL, Nicolau A, Veidenbaum A. Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors. In: Proceedings of the 2009 International Joint Conference on Neural Networks, Atlanta, GA, USA, 14-9 June, 2009; p. 3201-8.

Nasse F, Thurau C, Fink GA. Face Detection Using GPU-Based Convolutional Neural Networks. In: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns. Berlin, Heidelberg, Germany: Springer-Verlag; 2009. p. 83-90.

Uetz R, Behnke S. Large-Scale Object Recognition with CUDA-Accelerated Hierarchical Neural Networks. In: Proceedings of 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. Vol. 1. Shanghai, China, 20-2 November, 2009; p. 536-41.

Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K. A performance study of general-purpose applications on graphics processors using CUDA. J Parallel Distrib Comput 2008;68(10):1370-80.

Jang H, Park A, Jung K. Neural Network Implementation Using CUDA and OpenMP. In: Proceedings of the 2008 Digital Image Computing: Techniques and Applications, Canberra, ACT, Australia, 1-3 December; 2008. p. 155-61.

Kim J, Hwangbo M, Kanade T. Realtime Affine-Photometric KLT Feature Tracker on GPU in CUDA Framework. In: Proceedings of IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan, 27 September, 4 October; 2009. p. 886-93.

Oh S, Jung K. View-point insensitive human pose recognition using neural network and CUDA. World Acad Sci Eng Technol 2009;60:723-6.

Schwarz M, Stamminger M. Fast GPU-based adaptive tessellation with CUDA. Comput Graph Forum 2009;28(2):365-74.

Simek V, Asn RR. GPU Acceleration of 2D-DWT Image Compression in MATLAB with CUDA. In: Proceedings of the 2008 2nd UKSIM European Symposium on Computer Modeling and Simulation, Liverpool, UK, 8-10 September; 2008. p. 274-7.

Stone SS, Haldar JP, Tsao SC, Hwu WM, Sutton BP, Liang ZP. Accelerating advanced MRI reconstructions on GPUs. J Parallel Distrib Comput 2008;68(10):1307-18.

Hwu WW. GPU Computing Gems Emerald Edition. 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2011.

Garcia-Rodriguez J, Angelopoulou A, García-Chamizo JM, Psarrou A, Orts-Escolano S, Morell-Gimenez V. Fast Autonomous Growing Neural Gas. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, CA, USA, 31 July, 5 August; 2011. p. 725-32.

Nickolls J, Dally WJ. The GPU computing era. IEEE Micro 2010;30:56-69.

Satish N, Harris M, Garland M. Designing Efficient Sorting Algorithms for Manycore GPUs. In: Proceedings of IEEE International Symposium on Parallel and Distributed Processing, Rome, Italy, 23-29 May; 2009. p. 1-10.

CUDA Programming Guide. Version 5.0, 2013. Available from: http://www.docs.nvidia.com/cuda/cuda-c-programming-guide. [Last accessed on 2013 Jun 12].

Kirk DB, Hwu WW. Programming Massively Parallel Processors: A Hands-on Approach. 1st ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2010.

Rai A. Attribute based level adaptive thresholding algorithm for object extraction. J Adv Robot 2015;1(2):64-8.

Rai A. Attribute based level adaptive thresholding algorithm (ABLATA) for image compression and transmission. J Math Comput Sci 2014;12:211-8.

Rai A. An introduction of smart self-learning shell programming interface. J Adv Shell Program 2015;1(2):3-6.

Rai A. Dynamic data flow based spatial sorting method for GPUs: Software based autonomous parallelization. Recent Trends Parallel Comput 2014;1(1):15-8.

Rai A. Dynamic pagination for efficient memory management over distributed computational architecture for swarm robotics. J Adv Shell Program 2014;1(2):1-4.

Rai A. Parallelizing mutations for genetic algorithm. Recent Trends Parallel Comput 2015;1(3):7-9.

Published

01-04-2017

How to Cite

Rai, A., and J. K. R. “CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE”. Asian Journal of Pharmaceutical and Clinical Research, vol. 10, no. 13, Apr. 2017, pp. 251-3, doi:10.22159/ajpcr.2017.v10s1.19651.

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