Open Access Open Access  Restricted Access Subscription or Fee Access

A Novel General Purpose GPU Computing Scheme for Automated Multi-Object Surveillance

Akriti Sahu, Rajesh Tiwari


In this study we presented a Graphic Processing Unit (GPU) based computing scheme for the division of large computational jobs such as of computer vision for the minimal processing time to facilitate the tracking of multiple objects in real time. The system comprises of the two objectives i.e., to dynamically parallelize the jobs and to reduce the CPU (Central processing unit) to GPU communication time. This will facilitate the other non-linear computing jobs to be logically broken into the parts based on the current memory usage criterion of the associated hardware devices. These features allow the robust computational environment for multi-level tasks like that of multiple visions, pattern matching and characterizations.

Keywords: Parallel computing, visual surveillance, GPU, multi-core, CPU

Cite this Article
Akriti Sahu, Rajesh Tiwari, A Novel General Purpose GPU Computing Scheme for Automated Multi-Object Surveillance, Recent Trends in Parallel Computing. 2015; 2(1): 1–5p.

Full Text:



Hu W.H.W., Tan T.T.T., Wang al. A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Trans. Syst. Man Cybern. Part C. 2004; 34: 334–352p.

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

Collins R.T., Lipton A.J., Kanade T. Introduction to the Special Section on Video Surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 2000; 22: 745–746p.

Howarth R.J., Buxton H. Conceptual Descriptions from Monitoring and Watching Image Sequences. Image Vis. Comput. 2000; 18: 105–135p.

Hu W., Xie D.,Tan T. A Hierarchical Self-Organizing Approach for Learning the Patterns of Motion Trajectories. IEEE Trans. Neural Netw. 2004; 15: 135–144p.

Tian Y., Tan T.N., Sun H.Z. A Novel Robust Algorithm for Real-Time Object Tracking. ActaAutom. 2002; 28: 851–853p.

Wu Y., Liu Q., Huang T.S. 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. 2000; 1106–1111p.

Howarth R.J., Buxton H. Analogical Representation of Space and Time. Image Vis. Comput. 1992; 10: 467–478p.

Brand M., Kettnaker, V. Discovery and Segmentation of Activities in Video. IEEE Trans. Pattern Anal. Mach. Intell. 2000; 22: 844–851p.

Garcia-Rodriguez J., Garcia Chamizo J.M. Surveillance and Human-Computer Interaction Applications of Self-Growing Models. Appl. Soft Comput. 2011; 11: 4413–4443p.

Nageswaran J.M., Dutt N., Krichmar J.L et al. 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. 2009; 3201–3208p.

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

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, Shanghai, China. 2009; 1: 536–541p.

Che S., Boyer M., Meng J. et al. A Performance Study of General-Purpose Applications on Graphics Processors Using CUDA. J. Parallel Distrib. Comput. 2008; 68: 1370–1380p.

Jang H., Park A., Jung K. Neural Network Implementation Using CUDA and Open MP. In Proceedings of the 2008 Digital Image Computing: Techniques and Applications, Canberra, ACT, Australia. 2008; 155–161p.

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. 2009; 886–893p.

Oh S., Jung K. View-Point Insensitive Human Pose Recognition Using Neural Network and CUDA. World Acad. Sci. Eng. Technol. 2009; 60: 723–726p.

Schwarz M., Stamminger M. Fast GPU-Based Adaptive Tessellation with CUDA. Comput. Gr. Forum. 2009; 28: 365–374p.

Simek V., Asn R.R. 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. 2008; 274–277p.

Stone S.S., Haldar J.P., Tsao al. Accelerating Advanced MRI Reconstructions on GPUs. J. Parallel Distrib. Comput. 2008; 68: 1307–1318p.

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

Garcia-Rodriguez J., Angelopoulou A., García-Chamizo al. Fast Autono-mous Growing Neural Gas. In Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), San Jose, CA, USA. 2011; 725–732p.

Nickolls J., Dally W.J. The GPU Computing Era. IEEE Micro. 2010; 30: 56–69p.

Satish N., Harris M., Garland M. Designing Efficient Sorting Algorithms for Manycore GPUs. In Proceedings of IEEE International Symposium on Parallel & Distributed Processing, Rome, Italy. 2009; 1–10p.

CUDA Programming Guide,Version 5.0, 2013. Available online: cuda-c-programming-guide/

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


  • There are currently no refbacks.

This site has been shifted to