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IJSTR >> Volume 6 - Issue 1, January 2017 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Comparison Of Processing Time Of Different Size Of Images And Video Resolutions For Object Detection Using Fuzzy Inference System

[Full Text]

 

AUTHOR(S)

Yogesh Yadav, Rajas Walavalkar, Sagar Suchak, Abhishek Yedurkar, Swapnil Gharat

 

KEYWORDS

Image Processing, Fuzzy Interference System, Image Resolution, MOG2(Measure of Gaussians), BSM (Background Subtraction Method),

 

ABSTRACT

Object Detection with small computation cost and processing time is a necessity in diverse domains such as :traffic analysis, security cameras, video surveillance etc .With current advances in technology and decrease in prices of image sensors and video cameras , the resolution of captured images is more than 1MP and has higher frame rates. This implies a considerable data size that needs to be processed in a very short period of time when real-time operations and data processing is needed. Real time video processing with high performance can be achieved with GPU technology. The aim of this study is to evaluate the influence of different image and video resolutions on the processing time, number of objects detections and accuracy of the detected object. MOG2 algorithm is used for processing video input data with GPU module. Fuzzy interference system is used to evaluate the accuracy of number of detected object and to show the difference between CPU and GPU computing methods.

 

REFERENCES

[1] Chulian Zhang, Hamed Tabkhi and Gunar Schirner, “A GPU-based Algorithm-specific Optimization for High-performance Background Subtraction “.

[2] Prem Kumar.V, Barath.V, Prashanth.K, “Object counting and density calculation using matlab”.

[3] Antonios Georgantzoglou, Joakim da Silva and Rajesh Jena, “Image Processing with MATLAB and GPU”.

[4] Deepali Shinde, Mithilesh Said, PratiknShetty, Swapnil Gharat, “Optimizing real time GPU kernels using Fuzzy Inference System” IJARSE, Vol. No. 2, Issue No.9 , ISSN-2319-8354(E).

[5] Jingfei Kong,Martin Dimitrov, Yi Yang, Janaka Liyanage, Lin Cao, Jacob Staples, Mike Mantor, Huiyang Zhou, “Acclerating MATLAB Image Processing Toolbox Functions on GPUs”.

[6] V. Kastrinaki, M. Zervakis, K. Kalaitzakis, “A survey of Video processing techniques for traffic applications”, Image and Vision Computing 21(2003) 359 – 381.

[7] https://pythonprogramming.net/mog-background-reduction-python-opencv-tutorial.html

[8] http://in.mathworks.com/help/fuzzy/newfis.html

[9] http://opencv24pythontutorials.readthedocs.io/en/stable.htm