Intelligent Crowd Counting System with Gender Classification

Authors

  • Dr. Sheshang Degadwala  Associate Professor, Computer Department, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Pragnya Kulkarni  U.G. Scholar, Sigma Institute of Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Mansi Patel  U.G. Scholar, Sigma Institute of Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Kesha Bhatt  U.G. Scholar, Sigma Institute of Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dharvi Soni  U.G. Scholar, Sigma Institute of Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT2062140

Keywords:

Crowd, Classification, Cascading, HoG, HMM, Haarlick, SVM

Abstract

Evaluating the quantity of individuals in exceptionally bunched swarm scenes is an amazingly testing task because of genuine impediment and non-consistency dispersion in one group picture. Human Counting innovation can be summed up into two sorts of writing: identification strategies and tallying techniques. Conventional methodologies for swarm tallying from pictures depended available made portrayals to remove low-level highlights. These highlights were then mapped for checking or creating thickness maps by means of different tallying procedures. The identification-based model commonly utilizes sliding window-based recognition calculations to include individuals in a picture. This Project likewise correlation of various sex grouping strategies and utilization of various racial highlights, for example, eyes, nose, and mouth, and so on for Gender orientation characterization its applications in numerous regions like observing, reconnaissance, and business profiling, and human-PC cooperation video order assignments.

References

  1. M. Tong, L. Fan, H. Nan, and Y. Zhao, "Brilliant Camera Aware Crowd Counting through Multiple Task Fractional Stride Deep Learning †," 2019.
  2. S. Saxena, "Plan of individuals checking framework utilizing MATLAB," no. August, pp. 10–12, 2017.
  3. V. Chauhan, S. Kumar, and S. K. Singh, "Human Count Estimation in High Density Crowd Images and Videos."
  4. S. D. Pore and B. F. Momin, "Bidirectional People Counting System in Video Surveillance," vol. 416415, pp. 724–727, 2016.
  5. M. Sajid, A. Hassan, and S. A. Khan, "Group Counting Using Adaptive Segmentation in a Congregation," no. 5, 2016.
  6. Y. Zhang and Y. Mama, "Single-Image Crowd Counting by means of Multi-Column Convolutional Neural Network," 2016.
  7. M. A. Hassan, I. Pardiansyah, A. S. Malik, and I. Faye, "Improved People Counting System based Head-Shoulder Detection in Dense Crowd Scenario," 2010.
  8. T. Xu, X. Chen, G. Wei, and W. Wang, "Group Counting Using Accumulated HOG," pp. 1877–1881, 2016.
  9. J. Nalepa, J. Szymanek, and M. Kawulok, "Continuous People Counting from Depth Images," vol. 2, pp. 387–397, 2015.
  10. A. Co, A. Kara, and M. Parlaktuna, "Individuals Counting System by Using Kinect Sensor," 2015.
  11. Z. Yu, C. Shen, and L. Chen, "Gender orientation Classification of Full Body Images Based on the Convolutional Neural Network," pp. 707–711, 2017.
  12. "Gender orientation Classification in live recordings Jiale Chen , Sen Liu , Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China , Hefei 230027 , China."
  13. S. Lapuschkin and A. Folio, "Comprehension and Comparing Deep Neural Networks for Age and Gender Classification," 2017.
  14. S. Jia, T. Lansdall-government assistance, and N. Cristianini, "Gender orientation Classification by Deep Learning on Millions of Weakly Labeled Images," 2016.
  15. X. Wang, A. Mohd Ali and P. Angelov, "Gender orientation and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behavior," 2017 third IEEE International Conference on Cybernetics (CYBCONF), Exeter, 2017, pp. 1-6.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Sheshang Degadwala, Pragnya Kulkarni, Mansi Patel, Kesha Bhatt, Dharvi Soni, " Intelligent Crowd Counting System with Gender Classification, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.440-447, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT2062140