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An Automated Attendance System: A Technique using Background Subtraction and Color Image Processing of RGB Planes


  • School of Computer Science & Engineering, Lovely Professional University, Phagwara – 144411, Punjab, India


Background/Objectives: The objective of this paper is to introduce an automated attendance marking system for LPU using CCTV cameras. It detects the students sitting in the class and takes the attendance automatically without any human assistance and count total number of students present in the class also. Methods/Statistical Analysis: An enhanced detection algorithm is proposed that is having two phases: color image processing of RGB planes and modified background subtraction of image which executed simultaneously for object detection. Before detection process, noise is removed from images by using log function which uniformly distributes the brightness over the image. To count the number of detected objects, a counter is used which calculates total number of objects present in an image. The Automatic ROI detection algorithm is also proposed which detects region of interest automatically on the basis of repeated regular pattern of object. Findings: The system able to count and mark attendance of students sitting in class room. Implementation shows that our proposed approach has 89.5% accuracy with less false positive and false negative rate as compared to other traditional approaches. The Automatic ROI detection algorithm also detects the region of interest with considerably high accuracy. Application/Improvements: The proposed system will easy to deploy in various schools, colleges, universities and other educational institutes with minimal setup cost where CCTV cameras are already installed in class rooms.


Background Subtraction, Log Function, Object Detection, RGB Color Image Processing, Region of Interest.

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  • Khurana K, Awasthi R. Techniques for object recognition in images and multi-object detection. International Journal of Advanced Research in Computer Engineering and Technology. 2013 Apr;2(4):1383–88.
  • Hussin R, Juhari MR, Kang NW, Ismail RC, Kamarudin A. Digital image processing techniques for object detection from complex background image. Procedia Engineering. 2012; 41:340–4.
  • Dharani S, Gowri S, Ramya S. Human segmentation using Haar-classifier. International Journal of Engineering Research and Applications. 2014 Jul; 4(7):89–93.
  • Tareque MH, Al Hasan AS. Human lips-contour recognition and tracing. International Journal of Advanced Research in Artificial Intelligence. 2014; 3(1):47–51.
  • Sujatha B, Santhanam T. Classical flexible lip model based relative weight finder for better lip reading utilizing multi aspect lip geometry. Journal of Computer Science. 2010; 6(10): 1065–9.
  • Sreenivas DK, Reddy CS, Sreenivasulu G. Contour approximation of image recognition by using curvature scale space and invariant-moment based method. International Journal of Advances in Engineering and Technology. 2014 May; 7(2):359–71.
  • Anam S, Uchino E, Suetake N. Image boundary detection using the modified level set method and a diffusion filter. Procedia Computer Science. 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems. 2013; 22:192–200.
  • Leordeanu M, Sukthankar R, Sminchisescu C. Generalized boundaries from multiple image interpretations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2014 Jul; 36(7):1312–24.
  • Bagherpour P, Cheraghi SA, Mokji MBM. Upper body tracking using KLT and Kalman filter. Procedia Computer Science. Proceedings of the International Neural Network Society Winter Conference. 2012; 13:185–91.
  • Ikizler N, Cinbis RG, Duygulu P. Human action recognition with line and flow histograms. 19th International Conference on Pattern Recognition; 2008. p. 1–4.
  • Kermani E, Asemani D. A robust adaptive algorithm of moving object detection for video surveillance. EURASIP Journal on Image and Video Processing; 2014. p. 1–9.
  • Yang JB, Shi M, Yi QM. A new method for motion target detection by background subtraction and update. Physics Procedia. International Conference on Medical Physics and Biomedical Engineering. 2012; 33:1768–75.
  • Maini R, Aggarwal DH. Study and comparison of various image edge detection techniques. International Journal of Image Processing. 2013;1(3):1–12.
  • Guo L, Liao Y, Luo D. Generic object detection using improved gentleboost classifier. Physics Procedia. International Conference on Solid State Devices and Materials Science. 2012; 25:1528–35.
  • Ma Y, Wu W, He Q. Algorithm for object detection using multi-core parallel. Physics Procedia. International Conference on Medical Physics and Biomedical Engineering. 2012; 33:455–61.
  • Aydin D, Ugur A. Extraction of flower regions in color images using ant colony Optimization. Procedia Computer Science. 2011; 3:530–36.
  • Yue Y, An Z, Wu H. Adaptive targets-detecting algorithm based on LBP and background modeling under complex scenes. Procedia Engineering. 2011; 15:2489–94.
  • Dash A, Kanungo P, Mohanty BP. A modified gray level co-occurrence matrix based thresholding for object background classification. Procedia Engineering. International Conference on Communication Technology and System Design. 2012; 30(2011):85–91.
  • Wang L, Yung NH. Crowd counting and segmentation in visual surveillance. 16th IEEE International Conference on Image Processing. 2009; 4:2573–76.
  • Zhao L, Davis LS. Closely coupled object detection and segmentation. Proceedings of the Tenth IEEE International Conference on Computer Vision. 2005; 1:454–61.
  • Wu B, Nevatia R. Tracking of multiple, partially occluded humans based on static body part detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006; 1:951–8.
  • Reddy MR, Vaithiyanathan V, Karthikeyan B, Venkatam TG. Change detection in video using pixel based parametric analysis. Indian Journal of Science and Technology. 2015; 8(35):5–9. DOI: 10.17485/ijst/2015/v8i35/80061.
  • Mehdi N, Ghodrat S, Amin E, Amanollah A. A new skin color detection approach based on Fuzzy expert system. Indian Journal of Science and Technology. 2015; 8(21):1–11. DOI: 10.17485/ijst/2015/v8i21/50606.


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