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Insights to Video Analytic Modelling Approach with Future Line of Research

by Madhu Chandra G., Sreerama Reddy G. M.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 147 - Number 7
Year of Publication: 2016
Authors: Madhu Chandra G., Sreerama Reddy G. M.
10.5120/ijca2016911167

Madhu Chandra G., Sreerama Reddy G. M. . Insights to Video Analytic Modelling Approach with Future Line of Research. International Journal of Computer Applications. 147, 7 ( Aug 2016), 15-24. DOI=10.5120/ijca2016911167

@article{ 10.5120/ijca2016911167,
author = { Madhu Chandra G., Sreerama Reddy G. M. },
title = { Insights to Video Analytic Modelling Approach with Future Line of Research },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 147 },
number = { 7 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 15-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume147/number7/25664-2016911167/ },
doi = { 10.5120/ijca2016911167 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:51:15.597679+05:30
%A Madhu Chandra G.
%A Sreerama Reddy G. M.
%T Insights to Video Analytic Modelling Approach with Future Line of Research
%J International Journal of Computer Applications
%@ 0975-8887
%V 147
%N 7
%P 15-24
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The area of Video analytics has made some significant improvement due to advancement in image processing and datamining techniques. However, the inclination is still towards image contents and less to mined contents owing to many unsolved issues. Although concept of mining is more than 2 decade old, but mining approaches are yet to be standardized in the area of video surveillance system. With evolution of newer set of challenges in video capturing, existing mining models finds itself less applicable due to unstructured format of dynamic frames. Hence, this paper discusses about video analytics and presents a brief discussion of frequently used mining approaches in video as well as discussed some recent studies in this direction in order to scale the degree of effectiveness in existing system. The paper also presents research gap and provided solution as future line of research as a possible way to overcome the research gap.

References
  1. D.T. Larose, C.D. Larose, "Data Mining and Predictive Analytics", John Wiley & Sons, pp. 824, 2015
  2. M. Hofmann, R. Klinkenberg, "RapidMiner: Data Mining Use Cases and Business Analytics Applications", CRC Press, pp. 525, 2013
  3. Karâa, Wahiba Ben Abdessalem, "Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes", IGI Global, pp. 335, 2015
  4. O. Vermesan, P. Friess, "Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems",River Publishers, Technology & Engineering, pp. 364, 2013
  5. Marbán, Óscar, Gonzalo Mariscal, and Javier Segovia. "A data mining & knowledge discovery process model." Data Mining and Knowledge Discovery in Real Life Applications 2009 (2009): 8.
  6. Hakeem, Asaad, Himaanshu Gupta, Atul Kanaujia, Tae Eun Choe, Kiran Gunda, Andrew Scanlon, Li Yu et al. "Video analytics for business intelligence." In Video Analytics for Business Intelligence, pp. 309-354. Springer Berlin Heidelberg, 2012.
  7. G. Khanvilkar, D.B. Meshram, “Video Data Mining: Event Detection from the Association Perspective using FP-growth Tree”, International Journal of Engineering Research and Applications (IJERA) Volume, 1, 2011
  8. Kohun, Frederick G., and Gary J. DeLorenzo. "Simplified Procedures in Digital Video Editing: Concepts and Technological Alternatives", Retrieved, 22nd June, 2016
  9. N. Dimitrova, H-J. Zhang, B. Shahraray, I. Sezan, T. Huang, and A. Zakhor, "Applications of video-content analysis and retrieval." IEEE multimedia 9, no. 3, 42-55, 2002.
  10. R. Agrawal, T. Imielinski, and A. Swami, “Database mining: A performance perspective”, IEEE transactions on knowledge and data engineering, Vol./ 5, no. 6 , pp. 914-925, 1993.
  11. B.V. Patel and B. B. Meshram. "Content based video retrieval systems."arXiv preprint arXiv:1205.1641, 2012
  12. G. Karypis, E-H. Han, and V. Kumar. "Chameleon: Hierarchical clustering using dynamic modeling." Computer 32, no. 8, 68-75, 1999.
  13. D. Saravanan, and S. Srinivasan. "Video image retrieval using data mining techniques." Journal of computer applications (JCA) 1 39-42, 2012
  14. S-C. Chen, M-L. Shyu, C. Zhang, and J. Strickrott, "Multimedia Data Mining for Traffic Video Sequences." In MDM/KDD, pp. 78-86. 2001
  15. S.P. Algur, P. Bhat, "Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects", I.J. Information Engineering and Electronic Business, pp. 69-77, 2016
  16. V.A. Paliwal, N. R. Adam, H. Xiong, and C. Bornhovd. "Web service discovery via semantic association ranking and hyperclique pattern discovery." In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 649-652. IEEE Computer Society, 2006.
  17. C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. Luisa Damiani et al. "Semantic trajectories modeling and analysis." ACM Computing Surveys (CSUR) 45, no. 4, pp. 42, 2013.
  18. J. Han, J. Pei, and X.Yan, "Sequential pattern mining by pattern-growth: Principles and extensions." In Foundations and Advances in Data Mining, pp. 183-220. Springer Berlin Heidelberg, 2005.
  19. K. Saxena, and D. S. Rajpoot. "A Way to Understand Various Patterns of Data Mining Techniques for Selected Domains." arXiv preprint arXiv:0911.0781,2009
  20. R. Srikant, and R. Agrawal, “Mining sequential patterns: Generalizations and performance improvements”, In International Conference on Extending Database Technology, pp. 1-17. Springer Berlin Heidelberg, 1996.
  21. T. Srivastava, P. Desikan, and V. Kumar, “Web mining–concepts, applications and research directions”, In Foundations and advances in data mining,. Springer Berlin Heidelberg, pp. 275-307, 2005.
  22. R. Jain, and G.N Purohit, "Page ranking algorithms for web mining."International journal of computer applications 13, no. 5, pp.22-25, 2011.
  23. J.H. Oh, J.K. Lee, and S.k. Kote. "Real time video data mining for surveillance video streams." In Pacific-Asia conference on knowledge discovery and data mining, pp. 222-233. Springer Berlin Heidelberg, 2003.
  24. J. Meng, J. Yuan, M. Hans, and Y. Wu, "Mining motifs from human motion." In Proc. of EUROGRAPHICS, vol. 8. 2008
  25. G.M. Weiss, “Data mining in telecommunications”, In Data Mining and Knowledge Discovery Handbook (pp. 1189-1201). Springer US, 2005
  26. D. Pallez, L. Brisson, and T. Baccino. "Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC." arXiv preprint arXiv:0803.3186, 2008
  27. H. Banaee, M. U. Ahmed, and A. Loutfi, "Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges." Sensors 13, no. 12, 17472-17500, 2013
  28. K.I. Asad, T. Ahmed, and Md S. Rahman. "Movie popularity classification based on inherent movie attributes using C4. 5, PART and correlation coefficient." In Informatics, Electronics & Vision (ICIEV), 2012 International Conference, pp. 747-752. IEEE, 2012.
  29. S. Kabinsingha, S. Chindasorn, and C. Chantrapornchai. "Movie Rating Approach and Application Based on Data Mining." International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, 2012
  30. M. Saraee, S. White, and J. Eccleston. "A data mining approach to analysis and prediction of movie ratings." Transactions of the Wessex Institute, pp.343-352, 2004
  31. C.K.A. Bhatt, and M. S. Kankanhalli. "Multimedia data mining: state of the art and challenges." Multimedia Tools and Applications51, no. 1, pp.35-76, 2011.
  32. P. Thirumurugan, and S. Hasan Hussain, "Event detection in videos using data mining techniques." International Journal of Computer Science and Information Technologies 3, no. 2, pp.3473-3475, 2012
  33. A. Divakaran, K. Miyahara, K. A. Peker, R. Radhakrishnan, and Z. Xiong. "Video mining using combinations of unsupervised and supervised learning techniques." In Electronic Imaging 2004, pp. 235-243. International Society for Optics and Photonics, 2003.
  34. S. Shekhar, R. Michael Evans, M. J. Kang, and P. Mohan. "Identifying patterns in spatial information: A survey of methods." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 3, 193-214, 2011
  35. J.Y. Lee, and W. Hoff, "Activity identification utilizing data mining techniques." In Motion and Video Computing, WMVC'07. IEEE Workshop, pp. 12-12, 2007
  36. P. Angelov, P. S-Tehran, C. Clarke, “AURORA: autonomous real-time on-board video analytics”, Neural Comput & Applica, 2016
  37. V.N.M. Aradhya and M. S. Pavithra, “A comprehensive of transforms, Gabor filter and k-means clustering for text detection in images and video", Applied Computing and Informatics, 2014
  38. A. Aryanfar, R. Yaakob, A. A. Halin, N. Sulaiman, K. A. Kasmiran, and L. Mohammadpour, “Multi-View Human Action Recognition Using Wavelet Data Reduction and Multi-Class Classification”, Procedia Computer Science, Vol. 62, pp.585-592, 2015.
  39. A.B. Ayed, M. B. Halima, and A. M. Alimi, “MapReduce Based Text Detection in Big Data Natural Scene Videos”, Procedia Computer Science, Vol. 53, pp.216-223, 2015.
  40. P. Vollucci, B. Le, J. Lai, W. Cai, Y. Ye, G. Necula, and D. Wroblewski, “Online Video Data Analytics”, 2015
  41. C.H.E. N. Qing, Y. Chen, D. Liu, C. Shi, Y. Wu, and H. Qu, “PeakVizor: Visual Analytics of Peaks in Video Clickstreams from Massive Open Online Courses”, 2015.
  42. Y.B. Kim, S. J. Kang, Sang Hyeok Lee, Jang Young Jung, Hyeong Ryeol Kam, Jung Lee, Young Sun Kim, Joonsoo Lee, and Chang Hun Kim. "Efficiently detecting outlying behavior in video-game players." PeerJ 3 (2015): e1502.
  43. B. Mao, J.He, J. Cao, S.W. Bigger, and T. Vasiljevic, “A framework for food traceability information extraction based on a video surveillance system”, Procedia Computer Science, Vol. 55, pp.1285-1292, 2015.
  44. M. Shao, and Y.Fu, “Deeply Self-Taught Multi-View Video Analytics Machine for Situation Awareness”, AFA Cyber Workshop, White Paper, 2015
  45. T. Xu, W. Xiang, Q. Guo, and L. Mo, “Mining cloud 3D video data for interactive video services”, Mobile Networks and Applications, Vol. 20, No. 3, pp. 320-327, 2015.
  46. F. Riahi, O. Schulte, and Q. Li, “A Proposal for Statistical Outlier Detection in Relational Structures”, In AAAI Workshop: Statistical Relational Artificial Intelligence, 2014.
  47. D. Potapov, M. Douze, Z. Harchaoui, and C. Schmid, “Category-specific video summarization”, In European conference on computer vision, pp. 540-555, 2014.
  48. A.K. Thilagavathy, Aarthi, and A. Chilambuchelvan, “Scene text extraction from videos using hybrid approach”, In Advances in Computing and Information Technology, Springer Berlin Heidelberg, pp. 739-748 2013.
  49. Y. Cong, J. Yuan, and Y. Tang, “Video anomaly search in crowded scenes via spatio-temporal motion context”, IEEE transactions on information forensics and security, Vol. 8, No. 10, pp.1590-1599, 2013.
  50. M. Bertini, A.D. Bimbo, and L. Seidenari, “Multi-scale and real-time non-parametric approach for anomaly detection and localization", Computer Vision and Image Understanding,  Vol. 116, No. 3, pp. 320-329, 2012.
  51. F. Jiang, J. Yuan, S.A. Tsaftaris, and A. K. Katsaggelos, “Anomalous video event detection using spatiotemporal context”, Computer Vision and Image Understanding, Vol. 115, No. 3, pp. 323-333, 2011.
  52. Y. Deldjoo, M. Elahi, P. Cremonesi, F. Garzotto, P. Piazzolla, and M. Quadrana, “Content-Based Video Recommendation System Based on Stylistic Visual Features”,  Journal on Data Semantics, pp.1-15, 2016.
  53. H. Nomiya, A. Morikuni, and T. Hochin, “Unsupervised Emotional Scene Detection for Lifelog Video Retrieval based on Gaussian Mixture Model”, Procedia Computer Science, Vol. 22, pp. 375-384, 2013.
  54. C. Devasena, Lakshmi, and M. Hemalatha, “Video Mining using LIM Based Clustering and Self Organizing Maps”, Procedia Engineering, Vol. 30, (2012): 913-921.
  55. A. Kumar, A. K. Kaushik, and R. L. Yadav, “A robust and fast text extraction in images and video frames”, In Advances in Computing, Communication and Control, Springer Berlin Heidelberg, pp. 342-348, 2011.
  56. V. Saligrama, J. Konrad, and P-M. Jodoin, “Video anomaly identification”, IEEE Signal Processing Magazine, Vol. 27, No. 5, pp. 18-33, 2010
  57. A. Sureka, P. Kumaraguru, A. Goyal, and S. Chhabra, “Mining youtube to discover extremist videos, users and hidden communities”, In Asia Information Retrieval Symposium Springer Berlin Heidelberg, pp. 13-24, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Analytics Data Mining Knowledge Discovery Semantics Video.