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Automatic Pattern Co-Location Detection in Video Streams-Real time implementation

by C.V. Jayaram, B.K. Raghavendra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 4
Year of Publication: 2023
Authors: C.V. Jayaram, B.K. Raghavendra
10.5120/ijca2023922690

C.V. Jayaram, B.K. Raghavendra . Automatic Pattern Co-Location Detection in Video Streams-Real time implementation. International Journal of Computer Applications. 185, 4 ( Apr 2023), 5-11. DOI=10.5120/ijca2023922690

@article{ 10.5120/ijca2023922690,
author = { C.V. Jayaram, B.K. Raghavendra },
title = { Automatic Pattern Co-Location Detection in Video Streams-Real time implementation },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 4 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 5-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number4/32691-2023922690/ },
doi = { 10.5120/ijca2023922690 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:25:14.150209+05:30
%A C.V. Jayaram
%A B.K. Raghavendra
%T Automatic Pattern Co-Location Detection in Video Streams-Real time implementation
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 4
%P 5-11
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper offers a thorough analysis of automated co-located pattern detection in video streaming. Many flaws, including face recognition, pattern or object recognition, scene comprehension, co-located pattern recognition, etc., have historically developed as a result of pattern recognition procedures. In addition to presenting an exhaustive state of the art in the field, our review study also discusses several encounters and trials relating to its applications and system. Many applications are discussed in great detail in this study.

References
  1. Jin SoungYoo and Shashi Shekar, A Joinless Approch for Mining Spatial Co-location Patterns, IEEE Transactions on Knowledge & Data Engg. Vol.18, NO.10, pp-1323-1337, October 2006
  2. Y. Huang, J. Pei, and H. Xiong. Mining co-location patterns with rare events from spatial data sets. Geoinformatica, 10(3):239-260, 2006.
  3. Gunnur Jansson and Magnus Ostrom, The effect of co-location of visual and haptic space on judgments of Form. Proceedings of Euro Haptics, Munich Germany, june5-7,2004
  4. J. Yoo and S. Shekhar, “A Partial Join Approach for Mining Co-location Patterns,” Proc. ACM Int’l Symp. Advances in Geographic Information Systems (ACM-GIS), 2004.
  5. S. Shekhar and Y. Huang, “Co-location Rules Mining: A Summary of Results,” Proc. Int’l Symp. Spatio and Temporal Database (SSTD),2001.
  6. K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proc. Fourth Int’l Symp. Large Spatial Data Bases, pp. 47-66, 1995.
  7. S. Shekhar and S. Chawla, Spatial Databases: A Tour. Prentice Hall,2003.
  8. J. Yoo and S. Shekhar, “A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results,” Proc. IEEE Int’l Conf. Data Mining (ICDM), 2005.
  9. Y. Huang, J. Pei, and H. Xiong. Mining co-location patterns with rare events from spatial data sets. Geoinformatica, 10(3):239{260, 2006.
  10. J. S. Yoo, S. Shekhar, J. Smith, and J. P. Kumquat. A partial join approach for mining co-location patterns. Proceedings of GIS, pages 241-249, 2004.
  11. Y. Huang, S. Shekhar, and H. Xiong. Discovering co-location patterns from spatial data sets: A general approach. TKDE, 16(12):1472-1485, 2004.
  12. Y. Huang, H. Xiong, S. Shekhar, and J. Pei. Mining content co-location rules without a support threshold. Proceedings of SAC, pages 497-501, 2003.
  13. Y. Huang and P. Zhang. On the Relationship Between Clustering and Spatial Co-location Pattern Mining. Proceedings of ICTAI, pages 513-522, 2006.
  14. X. Zhang, N. Mamoulis, D. W. Cheung, and Y. Shou. Fast mining of spatial collocations. Proceedings of SIGKDD, pages 384-393, 2004.
  15. K. Koperski and J. Han. Discovery of spatial association rules in geographic information databases. Proceedings of SSD, pages 47-66, 1995.
  16. Y. Morimoto. Mining frequent neighboring class sets in spatial databases. Proceedings of SIGKDD, pages 353-358, 2001.
  17. Öström, M.: The importance of co-location for perception of form. Undergraduate thesis. Uppsala University, Department of Information Technology, Uppsala, Sweden (2003)
  18. L. Wixson. Detecting salient motion by accumulating directionary-consistenct flow. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(8):774–780,August 2000.
  19. C. Wren, A. Azarbaygaui, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):780–785, July 1997.
  20. L. Li, W. M. Huang, I. Y. H. Gu, and Q. Tian. Foreground object detection in changing background based on color co-occurrence statistics. In Proceedings IEEE Workshop on Application of Computer Vision, pages 269–274, 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Pattern Recognition Co-location Object detection Frame extraction

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