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Human Activity Recognition System to Benefit Healthcare Field by using HOG and Harris Techniques with K-NN Model

by Wala'a N. Jasim, Esra J. Harfash
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 40
Year of Publication: 2018
Authors: Wala'a N. Jasim, Esra J. Harfash
10.5120/ijca2018917045

Wala'a N. Jasim, Esra J. Harfash . Human Activity Recognition System to Benefit Healthcare Field by using HOG and Harris Techniques with K-NN Model. International Journal of Computer Applications. 180, 40 ( May 2018), 7-14. DOI=10.5120/ijca2018917045

@article{ 10.5120/ijca2018917045,
author = { Wala'a N. Jasim, Esra J. Harfash },
title = { Human Activity Recognition System to Benefit Healthcare Field by using HOG and Harris Techniques with K-NN Model },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 40 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number40/29393-2018917045/ },
doi = { 10.5120/ijca2018917045 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:11.458197+05:30
%A Wala'a N. Jasim
%A Esra J. Harfash
%T Human Activity Recognition System to Benefit Healthcare Field by using HOG and Harris Techniques with K-NN Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 40
%P 7-14
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The advancement of technology in recent years led to the development the human activities recognition (HAR) system in video . This type of system is one of an important areas for computer vision (CV) .This paper presents a system to help people who are suffered from a health problem and are stayed alone for long times especially the elderly , by recognizing three normal activities : ( walking , drinking and eating) and six abnormal activities : (headache , vomiting , fainting , renal colic , intestinal colic , angina ( , that are chosen from the daily life activities of elderly people . In this paper we proposed iterative thresholding for separating background from foreground and used two various techniques for features extraction Histogram Of Oriented Gradient (HOG) and Harris. Finally, K-Nearest Neighbors (K-NN) is used to classify normal and abnormal activities in video . The alarm system is activated when the system is recognized one of the abnormal activities by sending SMS email to the person who concerned with the status of the patient. The system is evaluated HOG with K-NN against with K-NN whether before and after using linear discriminant analysis (LDA) that is used to select the best features. Average recognition rate of HOG with K-NN before and after using LDA consecutively, 94.44% and 97.83% and average recognition rate of Harris with K-NN before and after using LDA Consecutively 87.65% and 93.51% for all normal and abnormal activities in our dataset.

References
  1. S. Vishwakarma and A. Agrawal, 2013, "A Survey on Activity Recognition and Behavior Understanding in Video Surveillance," The Visual Computer, Springer,Vol. 29, issue 10, pp. 983-1009.
  2. A. Taha, H. H. Zayed, M. Khalifa, and E.-S. M. El-Horbaty, 2015, "Human Activity Recognition for Surveillance Applications," in Proceedings of the 7th International Conference on Information Technology, pp. 577-586.
  3. S.-R. Ke, H. L. U. Thuc, Y.-J. Lee, J.-N. Hwang, J.-H. Yoo, and K.-H. Choi, 2013, "A Review on Video-based Human Activity Recognition," Computers, Multidisciplinary Digital Publishing Institute, Vol. 2, issue 2, pp. 88-131.
  4. Z. A. Khan and W. Sohn, 2011, "Abnormal Human Activity Recognition System Based on R-Transform and Kernel Discriminant Technique for Elderly Home Care," IEEE Transactions on Consumer Electronics,Vol. 57, issue 4, pp. 1843-1850 .
  5. H. Foroughi, H. S. Yazdi , H. Pourreza and M. Javidi , 2008, " AEigenspace-Based Approach for Human Fall Detection Using Image and Multi-class Support Vector Machine," , .ICSP 2008. 9th International Conference on. IEEE.
  6. C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, 2011, "Robust Video Surveillance for Fall Detection Based on Human Shape Deformation," IEEE Transactions on Circuits and Systems for Video technology,Vol. 21, issue 5, pp. 611-622.
  7. Z. A. Khan and W. Sohn, 2013, "A hierarchical Abnormal Human Activity Recognition System Based on R-Transform and Kernel Discriminant Analysis for Elderly Health Care," Computing, Springer, Vol. 95, issue 2, pp. 109-127.
  8. G. , R.C. and R.E. Woods, 2002, "Thresholding. Digital Image Processing," Pearson Prentice Hall.
  9. D. M., 2012, "Wavelet and Curvelet Based Thresholding Techniques for Image Denoising," International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), Vol. 1, issue 10, pp: 77-81.
  10. M. Seki, H. Fujiwara, and K. Sumi, 2000, "A Robust Background Subtraction Method for Changing Background," in Proceedings of Fifth IEEE Workshop in the Applications of Computer Vision, IEEE, pp. 207-213.
  11. E. B. T. Al-Abadi, 2014, "Gait Recognition System Using Two techniques: Support Vector Machine and Neural Network," M.Sc. Thesis, Department of Computer Science, University of Basra.
  12. N. Dalal and B. Triggs., 2005, "Histograms of oriented sgradients for human detection. in Computer Vision and Pattern Recognition," in Proceedings of the Conference on Computer Society and Pattern Recognition (CVPR), IEEE, pp. 886-893.
  13. S.K. Uma and S.B.J, 2015, "Feature Extraction for Human Detection Using HOG and CS-LBP Methods," in Proceedings of the National Conference in Electronics, Signals, Communication and Optimization, International Journal of Computer Applications, pp. 11-14.
  14. S. Kim and K. Cho, 2014, "Fast Calculation of Histogram of Oriented Gradient Feature by Removing Redundancy in Overlapping Block," Journal of Information Science and Engineering, Vol. 30, issue 6, p. 1719-1731.
  15. C. Harris, and M. Stephens, 1988, "A Combined Corner and Edge Detector," in Proceedings of the Alvey vision conference, Citeseer, pp. 147-152.
  16. M. Felsberg, A. Heyden, and N. Krüger, 2017, "Computer Analysis of Images and Patterns," in Proceedings of the 17th International Conference, Caip 2017, Springer, Proceedings, Part II.
  17. J. Loundagin, 2015, "Optimizing Harris Corner Detection on GPGPUs Using CUDA," M.Sc. Thesis, Department of Electrical Engineering, The Faculty of California Polytechnic State University.
  18. J. Malik, R. Dahiya, and G. Sainarayanan, 2011, "Harris Operator Corner Detection Using Sliding Window Method," International Journal of Computer Applications, Citeseer, Vol. 22, pp. 28-37.
  19. Z. Voulgaris and G.D. Magoulas. 2008, "Dimensionality Reduction for Feature and Pattern Selection in Classification Problems," in Proceedings of the Third International Multi-Conference in Computing in the Global Information Technology, IEEE, pp. 160-165.
  20. A. Ghodsi, 2006, "Dimensionality Reduction a Short Tutorial," Department of Statistics and Actuarial Science, Univ. of Waterloo, Ontario, Canada, Vol. 37, pp. 1-21.
  21. E. J. HARFASH, 2016, "Face Recognition System Using PCA, LDA, Kernel PCA and Kernel LDA," International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), Vol. 6, issue 5, pp. 9-20.
  22. B. Shaw, and T. Jebara., 2009, "Structure Preserving Embedding," in Proceedings of the 26th Annual International Conference on Machine Learning, ACM, pp. 937-944.
  23. P.M.C. Guerreiro, 2008, "Linear Discriminant Analysis Algorithms," M.Sc. Thesis, Technical University of Lisbon, Portugal.
  24. S. Balakrishnama, and A. Ganapathiraju, 1998, "Linear Discriminant Analysis - a Brief Tutorial," Institute for Signal and information Processing, Vol. 18, pp. 1-8.
  25. G. Zhong, L.-N. Wang, X. Ling, and J. Dong, 2016, "An overview on Data Representation Learning: from Traditional Feature Learning to Recent Deep Learning," The Journal of Finance and Data Science, Elsevier, Vol. 2, issue 4, pp. 265-278.
  26. N.K. Kamila, 2015, "Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing," IGI Global.
  27. R. O. Duda, P. E. Hart, D. G. Stoke, 2012,"Pattern Classification", 2nd ed., John Wiley & Sons.
  28. J. Han, M. Kamber and J. Pei, 2011, "Data Mining: Concepts and Techniques," Elsevier.
  29. S. Kaghyan , and H. Sarukhanyan, 2012, "Activity Recognition Using K-nearest Neighbor Algorithm on Smartphone with TRI-AXIAL Accelerometer," International Journal of Informatics Models and Analysis (IJIMA), Vol. 1, issue 2, pp. 146-156.
  30. B. Liu, 2007, "Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data," Springer Science & Business Media.
  31. J. L. M. Iqbal, J. Lavanya, and S. Arun, 2015, "Abnormal Human Activity Recognition Using Scale Invariant Feature Transform," International Journal of Current Engineering and Technology,Vol. 5, issue 6, pp. 3748-3751.
Index Terms

Computer Science
Information Sciences

Keywords

Thresholding HOG Harris LDA K-NN

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