ICTACT Journal on Image and Video Processing

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Selected Single Face Tracking in Technically Challenging Different Background Video Sequences using Combined Features


Affiliations
1 Department of Computer Science and Engineering, Government Engineering College, Hassan, India
2 Department of Computer Science and Engineering, Kalpataru Institute of Technology, India
3 Department of Computer Science and Engineering, Rajeev Institute of Technology, India
     

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The commonly identified limitations of video face trackers are, the inability to track human face in different background video sequences with the conditions like occlusion, low quality, abrupt motions and failing to track single face when it contain multiple faces. In this paper, we propose a novel algorithm to track human face in different background video sequences with the conditions listed above. The proposed algorithm describes an improved KLT tracker. We collect Eigen, FAST as well as HOG features and combine them together. The combined features are given to the tracker to track the face. The algorithm being proposed is tested on challenging datasets videos and measured for performance using the standard metrics.

Keywords

Track Human Face, Different Background, Video Sequences, KLT, Combined Features.
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  • S. Ranganatha and Y.P. Gowramma, “Face Recognition Techniques: A Survey”, International Journal for Research in Applied Science and Engineering Technology, Vol. 3, No. 4, pp. 630-635, 2015.

  • S. Ranganatha and Y.P. Gowramma, “A Comprehensive Survey of Algorithms for Face Tracking in Different Background Video Sequence”, International Journal of Computer Applications, 2018.

  • P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 511-518, 2001.

  • P. Viola and M. Jones, “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol. 57, No. 2, pp. 137-154, 2004.

  • Jianbo Shi and Carlo Tomasi, “Good Features to Track”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 593-600, 1994.

  • E. Rosten and T. Drummond, “Fusing Points and Lines for High Performance Tracking”, Proceedings of IEEE International Conference on Computer Vision, pp. 1508-1515, 2005.

  • N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.

  • Stephen M. Smith and J. Michael Brady, “SUSAN-A New Approach to Low Level Image Processing”, International Journal of Computer Vision, Vol. 23, No. 1, pp. 45-78, 1997.

  • C. Harris and M. Stephens, “A Combined Corner and Edge Detector”, Proceedings of 4th Alvey Vision Conference, pp. 147-151, 1988.

  • D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 603-619, 2002.

  • K. Fukunaga and L.D. Hostetler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition”, IEEE Transactions on Information Theory, Vol. 21, No. 1, pp. 32-40, 1975.

  • Yizong Cheng, “Mean Shift, Mode Seeking, and Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, pp. 790-799, 1995.

  • G. Bradski, “Computer Vision Face Tracking for Use in a Perceptual User Interface”, Intel Technology Journal, pp. 12-21, 1998.

  • Bruce D. Lucas and Takeo Kanade, “An Iterative Image Registration Technique with an Application to Stereo Vision”, Proceedings of International Joint Conference on Artificial Intelligence, pp. 674-679, 1981.

  • Carlo Tomasi and Takeo Kanade, “Detection and Tracking of Point Features”, Available at: https://cecas.clemson.edu/~stb/klt/tomasi-kanade-techreport-1991.pdf.

  • S. Ranganatha and Y P Gowramma, “A Novel Fused Algorithm for Human Face Tracking in Video Sequences”, Proceedings of IEEE International Conference on Computation System and Information Technology for Sustainable Solutions, pp. 1-6, 2016.

  • Supriya Mangale, Ruchi Tambe and Madhuri Khambete, “Object Detection and Tracking in Thermal Video Using Directed Acyclic Graph (DAG)”, ICTACT Journal on Image and Video Processing, Vol. 8, No. 1, pp. 1566-1574, 2017.

  • S. Ranganatha and Y.P. Gowramma, “An Integrated Robust Approach for Fast Face Tracking in Noisy Real-World Videos with Visual Constraints”, Proceedings of IEEE International Conference on Advances in Computing, Communications and Informatics, pp. 772-776, 2017.

  • S. Ranganatha 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, Vol. 10, No. 8, pp. 19-35, 2018.

  • S. Ranganatha 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, Vol. 10, No. 8, pp. 39-53, 2018.

  • S. Ranganatha 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, Vol. 6, No. 9, pp. 349-354, 2018.

  • C. Sanderson and B.C. Lovell, “Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference”, Proceedings of International Conference on Biometrics, pp. 199-208, 2009.

  • M. Kim, S. Kumar, V. Pavlovic and H. Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.

  • T. Fawcett, “An Introduction to ROC Analysis”, Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874, 2006.


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  • Selected Single Face Tracking in Technically Challenging Different Background Video Sequences using Combined Features

Abstract Views: 195  |  PDF Views: 1

Authors

S. Ranganatha
Department of Computer Science and Engineering, Government Engineering College, Hassan, India
Y. P. Gowramma
Department of Computer Science and Engineering, Kalpataru Institute of Technology, India
G. N. Karthik
Department of Computer Science and Engineering, Rajeev Institute of Technology, India
A. S. Sharan
Department of Computer Science and Engineering, Government Engineering College, Hassan, India

Abstract


The commonly identified limitations of video face trackers are, the inability to track human face in different background video sequences with the conditions like occlusion, low quality, abrupt motions and failing to track single face when it contain multiple faces. In this paper, we propose a novel algorithm to track human face in different background video sequences with the conditions listed above. The proposed algorithm describes an improved KLT tracker. We collect Eigen, FAST as well as HOG features and combine them together. The combined features are given to the tracker to track the face. The algorithm being proposed is tested on challenging datasets videos and measured for performance using the standard metrics.

Keywords


Track Human Face, Different Background, Video Sequences, KLT, Combined Features.

References