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Reseach Article

Monitoring of People Entering and Exiting Private Areas using Computer Vision

by Vinay Kumar V., P. Nagabhushan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 15
Year of Publication: 2019
Authors: Vinay Kumar V., P. Nagabhushan
10.5120/ijca2019919544

Vinay Kumar V., P. Nagabhushan . Monitoring of People Entering and Exiting Private Areas using Computer Vision. International Journal of Computer Applications. 177, 15 ( Nov 2019), 1-5. DOI=10.5120/ijca2019919544

@article{ 10.5120/ijca2019919544,
author = { Vinay Kumar V., P. Nagabhushan },
title = { Monitoring of People Entering and Exiting Private Areas using Computer Vision },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2019 },
volume = { 177 },
number = { 15 },
month = { Nov },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number15/30972-2019919544/ },
doi = { 10.5120/ijca2019919544 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:56.517750+05:30
%A Vinay Kumar V.
%A P. Nagabhushan
%T Monitoring of People Entering and Exiting Private Areas using Computer Vision
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 15
%P 1-5
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Entry-Exit surveillance is a novel research problem that addresses security concerns when people attain absolute privacy in camera forbidden areas such as toilets and changing rooms that are basic amenities to the humans in public places such as Shopping malls, Airports, Bus and Rail stations. The objective is, if not inside these camera forbidden areas, from outside, the individuals are to be monitored to analyze the time spent by them inside and also the suspecting transformations in their appearances if any. In this paper, firstly, a pseudo-annotated dataset of a laboratory observation of people entering and exiting the camera forbidden area captured using two cameras as an extension of the state-of-theart single-camera based EnEx dataset is presented. Conventionally the proposed dataset is named EnExX. Next, a spatial transition based event detection to determine the entry or exit of individuals is presented with standard results by evaluating the proposed model using the proposed dataset and the publicly available standard video surveillance datasets that are hypothesized to Entry-Exit surveillance scenarios. The proposed dataset is expected to enkindle active research in Entry-Exit Surveillance domain.

References
  1. Dufour, Jean-Yves, Intelligent Video Surveillance Systems, John Wiley Publisher (2012)
  2. V, Vinay Kumar, Nagabhushan,P, (2019) Entry-Exit Video Surveillance: A benchmark dataset. In: Chaudhuri, B.B., Nakagawa, M., Khanna, P., Kumar, S. (Eds.) Proceedings of 3rd International Conference on Computer Vision & Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore
  3. Vinay Kumar V, P Nagabhushan, Entry-Exit event detection from video frames, International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.112-118, 2018.
  4. Vinay Kumar V, P Nagabhushan. Appearance invariant Entry- Exit matching using visual soft biometric traits. arXiv preprint arXiv:1909.05145 (2019).
  5. Cheng J., Yang J., Zhou Y. (2005) A Novel Adaptive Gaussian Mixture Model for Background Subtraction. In: Marques J.S., Prez de la Blanca N., Pina P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg
  6. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005
  7. Dollar, P., R. Appel, S. Belongie, and P. Perona. ”Fast Feature Pyramids for Object Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 36, Issue 8, 2014, pp. 15321545.
  8. Welch, Greg, and Gary Bishop, An Introduction to the Kalman Filter, TR 95041. University of North Carolina at Chapel Hill, Department of Computer Science.
  9. V. Garcia, E. Debreuve and M. Barlaud, ”Region-of-Interest Tracking Based on Keypoint Trajectories on a Group of Pictures,” 2007 International Workshop on Content-Based Multimedia Indexing, Bordeaux, 2007, pp. 198-203.
  10. C. Stauffer and W. E. L. Grimson, ”Adaptive background mixture models for real-time tracking,” Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), Fort Collins, CO, USA, 1999, pp. 246-252 Vol. 2. doi: 10.1109/CVPR.1999.784637
  11. R. B. Fisher, PETS04 Surveillance Ground Truth Data Set, Proc. Sixth IEEE Int. Work. on Performance Evaluation of Tracking and Surveillance (PETS04), pp 1-5, May 2004.
  12. Belloc, M.; Velastin, S.A.; Fernandez, R.; Jara, M.: ’Detection of People Boarding/Alighting a Metropolitan Train using Computer Vision’, IET Conference Proceedings, 2018, p. 5 (6 pp.)-5 (6 pp.), DOI: 10.1049/cp.2018.1281
  13. Rui Yao, Guosheng Lin, Shixiong Xia, Jiaqi Zhao, Yong Zhou.2019. Video Object Segmentation and Tracking: A Survey. arXiv preprint arXiv:1904.09172 (2019).
Index Terms

Computer Science
Information Sciences

Keywords

Entry-Exit Surveillance EnExX dataset Private areas Tracking