The week's pick
Reseach Article
Enhancing the Efficiency of moving Video Camera Vigilance using DBSCAN
by
Ajay Vikram Dev
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 36 |
| Year of Publication: 2019 |
| Authors: Ajay Vikram Dev |
10.5120/ijca2019918938
|
Ajay Vikram Dev . Enhancing the Efficiency of moving Video Camera Vigilance using DBSCAN. International Journal of Computer Applications. 178, 36 ( Jul 2019), 1-4. DOI=10.5120/ijca2019918938
@article{
10.5120/ijca2019918938,
author = {
Ajay Vikram Dev
},
title = { Enhancing the Efficiency of moving Video Camera Vigilance using DBSCAN },
journal = {
International Journal of Computer Applications
},
issue_date = { Jul 2019 },
volume = { 178 },
number = { 36 },
month = { Jul },
year = { 2019 },
issn = { 0975-8887 },
pages = {
1-4
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume178/number36/30772-2019918938/
},
doi = { 10.5120/ijca2019918938 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:52:22.013656+05:30
%A Ajay Vikram Dev
%T Enhancing the Efficiency of moving Video Camera Vigilance using DBSCAN
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 36
%P 1-4
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The author is attempting to build up a model for dynamic or moving camcorder vigilance utilizing Density Based Clustering and area sensors. The authors attempt to exploit the rich usefulness uncovered by the AI worldview in which the stochastic condition to learn is portrayed as a two dimensional diagram where the situation of an object can be given by its directions and coordinates. The author utilizes DBSCAN algorithm alongside sensor empowered test ground zone that keeps the X and Y co-ordinates of the moving objects. The approach of the author here is to catch ceaseless video of the densest cluster of objects moving together. One pragmatic use of such framework is a wild scene where gatherings of creatures are moving together to some goal. There will be a to somewhat disorderly aimless movement however we mean to catch just those creatures that are more prominent in number as a gathering and the camera should move imagining them. This can be accomplished by the DBSCAN algorithm.
References
- K.Kameshwanran, & Malarvizhi, K. Survey on Clustering Techniques in Data Mining. International Journal of Computer Science and Information Technologies , 5 .2 (2014), 2272-2276.
- Junaid, S., & Bhosle, K. Overview of Clustering Techniques. International Journal of Advanced Research in Computer Science and Software Engineering , 4.11 (2014), 621-624.
- Wagstaff, Kiri, et al. "Constrained k-means clustering with background knowledge." Icml. Vol. 1. 2001.
- Hartigan, John A., and Manchek A. Wong. "Algorithm AS 136: A k-means clustering algorithm." Journal of the Royal Statistical Society. Series C (Applied Statistics) 28.1 (1979): 100-108.
- Park, Hae-Sang, and Chi-Hyuck Jun. "A simple and fast algorithm for K-medoids clustering." Expert systems with applications 36.2 (2009): 3336-3341.
- Velmurugan, T., and T. Santhanam. "Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points." Journal of computer science 6.3 (2010): 363.
- Panda, Mrutyunjaya, and Manas Ranjan Patra. "A Hybrid clustering approach for network intrusion detection using cobweb and FFT." Journal of Intelligent systems 18.3 (2009): 229-246.
- Matsuoka, Hidehiro, and Hiroki Shoki. "Comparison of pre-FFT and post-FFT processing adaptive arrays for OFDM systems in the presence of co-channel interference." 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003.. Vol. 2. IEEE, 2003.
- Ng, Raymond T., and Jiawei Han. "E cient and E ective Clustering Methods for Spatial Data Mining." Proceedings of VLDB. 1994.
- Ng, Raymond T., and Jiawei Han. "CLARANS: A method for clustering objects for spatial data mining." IEEE Transactions on Knowledge & Data Engineering 5 (2002): 1003-1016.
- Gasch, Audrey P., and Michael B. Eisen. "Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering." Genome biology 3.11 (2002): research0059-1.
- Huang, Zhexue, and Michael K. Ng. "A fuzzy k-modes algorithm for clustering categorical data." IEEE Transactions on Fuzzy Systems 7.4 (1999): 446-452.
- Chaturvedi, Anil, Paul E. Green, and J. Douglas Caroll. "K-modes clustering." Journal of classification 18.1 (2001): 35-55.
- He, Zengyou, Xiaofei Xu, and Shengchun Deng. "Squeezer: an efficient algorithm for clustering categorical data." Journal of Computer Science and Technology 17.5 (2002): 611-624.
- Huang, Zhexue. "Extensions to the k-means algorithm for clustering large data sets with categorical values." Data mining and knowledge discovery 2.3 (1998): 283-304.
- Barbará, Daniel, Yi Li, and Julia Couto. "COOLCAT: an entropy-based algorithm for categorical clustering." Proceedings of the eleventh international conference on Information and knowledge management. ACM, 2002.
- Struyf, Anja, Mia Hubert, and Peter Rousseeuw. "Clustering in an object-oriented environment." Journal of Statistical Software 1.4 (1997): 1-30.
- Datta, Susmita, and Somnath Datta. "Comparisons and validation of statistical clustering techniques for microarray gene expression data." Bioinformatics 19.4 (2003): 459-466.
- De Amorim, Saul G., Jean-Pierre Barthélemy, and Celso C. Ribeiro. "Clustering and clique partitioning: simulated annealing and tabu search approaches." Journal of Classification 9.1 (1992): 17-41.
- Goil, Sanjay, Harsha Nagesh, and Alok Choudhary. "MAFIA: Efficient and scalable subspace clustering for very large data sets." Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 443. ACM, 1999.
- Ahiska, Yavuz. "Multiple-view processing in wide-angle video camera." U.S. Patent No. 7,450,165. 11 Nov. 2008.
- Diener, Neil R., David S. Kloper, and Anthony T. Collins. "Server and multiple sensor system for monitoring activity in a shared radio frequency band." U.S. Patent No. 7,184,777. 27 Feb. 2007.
- Belisle, Timothy, and Thomas Belisle. "Geo-location system, method and apparatus." U.S. Patent Application No. 11/381,097.
- Williams, Darin Scot. "Car-finder method and aparatus." U.S. Patent Application No. 12/157,889.
- Pandey. H, and Darbari. M, “Coalescence of Evolutionary Multi-Objective Decision making approach and Genetic Programming for Selection of Software Quality Parameter”, International Journal of Applied Information System (IJAIS), Foundation of Computer Science, New York, USA, Volume 7, No. 11, PP. ISSN: 2249-0868, Nov. 2014.
- Bansal. S and Pandey. H, “Develop Framework for selecting best Software Development Methodology”, International Journal of Scientific and Engineering Research, Volume 5, Issue 4, PP. 1067-1070, ISSN: 2229-5518, Apr. 2014.
- Srivastava. M and Pandey. H, “A Literature Review of E- Learning Model Based on Semantic Web Technology”, International Journal of Scientific and Engineering Research” Volume 5, Issue 10, PP. 174-178, ISSN: 2229-5518, Oct. 2014.
- Pandey. H, “A New NFA Reduction Algorithm for State Minimization Problem”, International Journal of Applied Information Systems (IJAIS), Foundation of Computer Science FCS, New York, USA, Volume 8, No.3, PP. 27-30, ISSN: 2249-0868, Feb. 2015.
- Pandey. H, “LR Rotation rule for creating Minimal NFA”, International Journal of Applied Information Systems (IJAIS), Foundation of Computer Science FCS, New York, USA, Volume 8, No.6, PP. 1-4, ISSN: 2249-0868, Apr. 2015.
Index Terms
Computer Science
Information Sciences
Keywords