CFP last date
20 January 2025
Reseach Article

Study the Effect of Threshold Value on Object Detection

by Khalil Ibrahim Alsaif, Raghad Hazim Hamid
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 16
Year of Publication: 2018
Authors: Khalil Ibrahim Alsaif, Raghad Hazim Hamid
10.5120/ijca2018916187

Khalil Ibrahim Alsaif, Raghad Hazim Hamid . Study the Effect of Threshold Value on Object Detection. International Journal of Computer Applications. 179, 16 ( Jan 2018), 10-13. DOI=10.5120/ijca2018916187

@article{ 10.5120/ijca2018916187,
author = { Khalil Ibrahim Alsaif, Raghad Hazim Hamid },
title = { Study the Effect of Threshold Value on Object Detection },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 16 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 10-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number16/28881-2018916187/ },
doi = { 10.5120/ijca2018916187 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:30.870201+05:30
%A Khalil Ibrahim Alsaif
%A Raghad Hazim Hamid
%T Study the Effect of Threshold Value on Object Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 16
%P 10-13
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The object detection on movie based on static camera using background subtraction mainly depends on threshold value of calculating the difference between current frame and previous one. Most of the research use fixed threshold value cause an error technique. In this research the threshold Value calculated depends on histogram gray level of the Frame. The result for moved object detection on the movie Enhanced in addition higher detection obtained than the previous technique.

References
  1. M. Haag and H. H. Nagel, “Incremental recognition of traffic situations from video image sequences,” Image Vis. Comput., vol. 18, no. 2, pp. 137– 153, Jan. 2000.
  2. G. Ginesu, D. D. Giusto, and V. Margner, “Detection of foreign bodies in food by thermal image processing,” IEEE Trans. Ind. Electron., vol. 51, no. 2, pp. 480–490, Apr. 2004.
  3. S. Park and J. Aggarwal, “A hierarchical Bayesian network for event recognition of human actions and interactions,” Multimedia Syst., vol. 10, no. 2, pp. 164–179, Aug. 2004.
  4. A. C. Shastry and R. A. Schowengerdt, “Airborne video registration and traffic-flow parameter estimation,” IEEE Trans. Intell. Transp. Syst., vol. 6, no. 4, pp. 391–405, Dec. 2005.
  5. J. Melo, A. Naftel, A. Bernardino, and J. Santos-Victor, “Detection and classification of highway lanes using vehicle motion trajectories,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 2, pp. 188–200, Jun. 2006.
  6. Amit, Y., Trouv_e, A. (2007). POP: Patchwork of parts models for object recognition. International Journal of Computer Vision 75(2) 267{282
  7. Jin, Y., Geman, S. (2006). Context and hierarchy in a probabilistic image model. IEEE CVPR 2006
  8. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9) 1627{1645
  9. Jadhav J., 2014," Moving Object Detection and Tracking for Video Survelliance", International Journal of Engineering Research and General Science, Volume 2, 7.
  10. Watve A," Object tracking in video scenes",10.
  11. J. Joshan Athanesious, P.Suresh, “Systematic Survey on Object Tracking Methods in Video”,International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) October 2012, 242-247.
  12. Birkett R., "VIDEO PROCESSING ROAD SAFETY SYSTEM", 2016, 0061046481, 72.
  13. Cheung S, Kamath C," Robust techniques for background subtraction in urban tra_c video",W-7405-Eng-48,12.
  14. Jeon, J., and Manmatha, 2004. ”Using maximum entropy for automatic image annotation” Springer link, Lecture Notes in Computer Science The, Vol. 3115/2004.
  15. Elgammal, A., Dura swami, R., Harwood, D., Davis, L, 2002. Background and foreground modeling using nonparametric kernel density for visual surveillance. Proceedings of the IEEE 90, 1151-1163.
  16. J. Z. Liu and W. Q. Li, 1993. "The automatic thresholding of gray-level pictures via two-dimensional Otsu method," IEEE Int. Computer Vision and Pattern Recognition.
Index Terms

Computer Science
Information Sciences

Keywords

Object detection Object tracking Background subtraction Threshold value evaluation