CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Comprehensive Survey of Algorithms for Face Tracking in different Background Video Sequence

by Ranganatha S, Y. P. Gowramma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 27
Year of Publication: 2018
Authors: Ranganatha S, Y. P. Gowramma
10.5120/ijca2018918134

Ranganatha S, Y. P. Gowramma . A Comprehensive Survey of Algorithms for Face Tracking in different Background Video Sequence. International Journal of Computer Applications. 181, 27 ( Nov 2018), 43-49. DOI=10.5120/ijca2018918134

@article{ 10.5120/ijca2018918134,
author = { Ranganatha S, Y. P. Gowramma },
title = { A Comprehensive Survey of Algorithms for Face Tracking in different Background Video Sequence },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 181 },
number = { 27 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number27/30112-2018918134/ },
doi = { 10.5120/ijca2018918134 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:25.524956+05:30
%A Ranganatha S
%A Y. P. Gowramma
%T A Comprehensive Survey of Algorithms for Face Tracking in different Background Video Sequence
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 27
%P 43-49
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Video processing is an interesting research zone in image processing. Face tracking is part of video processing, where the face regions need to be detected and tracked. In this paper, we present a survey of some of the familiar algorithms that are used for tracking the face(s) in different background challenging video sequences. Mean-Shift is an important algorithm that is based on the displacement of points. Improvisation of Mean-Shift lead to the development of CAMSHIFT; the latter is one of the robust chromatic tracking approach developed till date. KLT is an efficient point tracking algorithm. This paper also includes a survey of different motion estimation algorithms, which are classified as either pixel based or feature based. At the end, recent developments help in knowing the relevant works that are being carried out now a days.

References
  1. Ranganatha S and Dr. Y P Gowramma, “Face Recognition Techniques: A Survey”, International Journal for Research in Applied Science and Engineering Technology (IJRASET), ISSN: 2321-9653, Vol.3, No.4, pp.630-635, April 2015.
  2. K. Fukunaga and L. D. Hostetler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition”, in IEEE Trans. on Information Theory, Vol.21, No.1, pp.32-40, January 1975. DOI: 10.1109/TIT.1975.1055330
  3. Yizong Cheng, “Mean Shift, Mode Seeking, and Clustering”, in IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol.17, No.8, pp.790-799, August 1995. DOI: 10.1109/34.400568
  4. Bruce D. Lucas and Takeo Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, in Proc. of International Joint Conference on Artificial Intelligence, Vol.2, pp.674-679, August 1981.
  5. Carlo Tomasi and Takeo Kanade, “Detection and Tracking of Point Features”, Carnegie Mellon University Technical Report CMU-CS-91-132, April 1991.
  6. Jianbo Shi and Carlo Tomasi, “Good Features to Track”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.593-600, June 1994. DOI: 10.1109/CVPR.1994.323794
  7. Philip H.S. Torr and Andrew Zisserman, “Feature Based Methods for Structure and Motion Estimation”, ICCV Workshop on Vision Algorithms, pp.278-294, September 1999.
  8. Michal Irani and P. Anandan, “About Direct Methods”, ICCV Workshop on Vision Algorithms, pp.267-277, September 1999.
  9. Rui Xu, David Taubman, and Aous Thabit Naman, “Motion Estimation Based on Mutual Information and Adaptive Multi-scale Thresholding”, in IEEE Trans. on Image Processing, Vol.25, No.3, pp.1095-1108, March 2016. DOI:  10.1109/TIP.2016.2514488
  10. C. Je and H-M. Park, “Optimized Hierarchical Block Matching for Fast and Accurate Image Registration”, Signal Processing: Image Communication, Vol.28, No.7, pp.779-791, August 2013.
  11. M. H. G. Peeters, “Implementation of the Phase Correlation Algorithm: Motion Estimation in the Frequency Domain”, Practical Training Report, June 2003.
  12. Andrew Burton and John Radford, “Thinking in Perspective: Critical Essays in the Study of Thought Processes”, Routledge, ISBN: 0-416-85840-6, 1978.
  13. David H. Warren and Edward R. Strelow, “Electronic Spatial Sensing for the Blind: Contributions from Perception”, Springer, ISBN: 90-247-2689-1, 1985.
  14. James J. Gibson, “The Perception of the Visual World”, Houghton Mifflin, 1950.
  15. C.S. Royden and K.D. Moore, “Use of Speed Cues in the Detection of Moving Objects by Moving Observers”, Vision Research, pp.17–24, 2012. DOI: 10.1016/j.visres.2012.02.006
  16. Hans P. Moravec, “Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover”, Robotics Institute, Carnegie Mellon University Technical Report CMU-RI-TR3, September 1980.
  17. C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, in Proc. of 4th Alvey Vision Conference, Manchester, UK, pp.147-151, 1988.
  18. Martin A. Fischler and Robert C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Commun. ACM, Vol.24, No.6, pp.381–395, June 1981. DOI: 10.1145/358669.358692
  19. G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, pp.12-21, 1998.
  20. E. Emami and M. Fathy, “Object Tracking Using Improved CAMShift Algorithm Combined with Motion Segmentation”, in Proc. of 7th Iranian Machine Vision and Image Processing (MVIP), pp.1-4, 2011.
  21. Ranganatha S and Y P Gowramma, “An Integrated Robust Approach for Fast Face Tracking in Noisy Real-World Videos with Visual Constraints”, in Proc. of IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.772-776, September 2017. DOI: 10.1109/ICACCI.2017.8125935
  22. Aziz Makandar and Daneshwari Mulimani, “Analysis of Multiple Object Detection Using Kalman Filter in Sports Video”, IJCA Proc. of National Conference on Computer Science and Information Technology (NCCSIT), 2017(1):13-15, September 2018.
  23. Ranganatha S and Y P Gowramma, “A Novel Fused Algorithm for Human Face Tracking in Video Sequences”, in Proc. of IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp.1-6, October 2016. DOI: 10.1109/CSITSS.2016.7779430
  24. Ranganatha S and Y P Gowramma, “Development of Robust Multiple Face Tracking Algorithm and Novel Performance Evaluation Metrics for Different Background Video Sequences”, International Journal of Intelligent Systems and Applications (IJISA), Vol.10, No.8, pp.19-35, August 2018. DOI: 10.5815/ijisa.2018.08.03
  25. Ranganatha S and Y P Gowramma, “Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences”, International Journal of Image, Graphics and Signal Processing (IJIGSP), Vol.10, No.8, pp.39-53, August 2018. DOI: 10.5815/ijigsp.2018.08.05
  26. Ranganatha S and Y P Gowramma, “Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence”, International Journal of Information Technology and Computer Science (IJITCS), Vol.10, No.11, pp.39-48, November 2018. DOI: 10.5815/ijitcs.2018.11.04
  27. Ranganatha S and Y P Gowramma, “Selected Single Face Tracking in Technically Challenging Different Background Video Sequences Using Combined Features”, ICTACT Journal on Image and Video Processing (JIVP), Vol.9, No.2, November 2018.
  28. P. Viola and M. Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision (IJCV), Vol.57, pp.137-154, 2004.
  29. P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features”, in Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, USA, Vol.1, pp.511-518, December 2001. DOI: 10.1109/CVPR.2001.990517
  30. Ranganatha S and Y P Gowramma, “Image Training and LBPH Based Algorithm for Face Tracking in Different Background Video Sequence”, International Journal of Computer Sciences and Engineering (IJCSE), Vol.6, No.9, pp.349-354, September 2018. CrossRef-DOI: https://doi.org/10.26438/ijcse/v6i9.349354
  31. M. Kim, S. Kumar, V. Pavlovic, and H. Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, June 2008. DOI: 10.1109/CVPR.2008.4587572
  32. Yongkang Wong, Shaokang Chen, Sandra Mau, Conrad Sanderson, and Brian C. Lovell, “Patch-Based Probabilistic Image Quality Assessment for Face Selection and Improved Video-Based Face Recognition”, in proc. of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.74-81, June 2011. DOI: 10.1109/CVPRW.2011.5981881
  33. Ivan Laptev, Marcin Marszalek, Cordelia Schmid, and Benjamin Rozenfeld, “Learning Realistic Human Actions from Movies”, in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-8, June 2008. DOI: 10.1109/CVPR.2008.4587756
  34. https://in.mathworks.com/downloads/R2018a/toolbox/vision/visiondata.
  35. C. Sanderson and B.C. Lovell, “Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference”, Lecture Notes in Computer Science (LNCS), Vol.5558, pp.199-208, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Face Tracking Survey Algorithms Different Background Video Sequence Mean-Shift CAMSHIFT KLT Motion Estimation Recent Developments.

Powered by PhDFocusTM