Robust Color Tracking to Ambient Light Changes

Authors

  • Dario Jose Mendoza Chipantasi Departamento de Energía y Mecánica, Departamento de Ciencias Exactas, Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • Nancy Velasco Departamento de Energía y Mecánica, Departamento de Ciencias Exactas, Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • David Rivas Departamento de Energía y Mecánica, Departamento de Ciencias Exactas, Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.
  • Victor H. Andaluz Departamento de Energía y Mecánica, Departamento de Ciencias Exactas, Departamento de Eléctrica y Electrónica, Universidad de las Fuerzas Armadas ESPE, Sangolqui, Ecuador.

Keywords:

Robust Tracking, Histogram Recalculation, Color Tracking,

Abstract

The use of color is a good technique for tracking objects. This technique has offered many advantages related to the robustness over occlusions, rotations, different geometries and scales. However, there are many studies that claimed that this technique generates excessive computational cost, and loss of reference due to the similar background color or loss of reference due to drastic changes in lighting. Therefore, there is a need to create a robust algorithm to prevent all the disadvantages mentioned above. This algorithm was mainly built on the analysis of the hue and saturation channels in the histogram based on the color space (hue, saturation and brightness - HSV). To resolve the disadvantages, an automatic recalculation of the histogram at each n frames has been developed. The algorithm has been enhanced to make it able to do all the process in real-time in large images up 1920x1080 pixels. The possible use of the algorithm is for controlling an automatic unmanned vehicle that will track a person.

References

Danelljan, M., Khan, F., Felsberg, M., & Weijer, J. 2014. Adaptive Color Attributes for Real Time Visual Tracking. IEEE Conference on Computer Vision and Pattern Recognition. 1090-1097.

Mukhtar, A., Xia, L. 2014. Target Tracking Using Color Based Particle Filter. 2014 5th International Conference on IEEE En Intelligent and Advanced Systems, (ICIAS). 1-6.

Matsumoto, T., Kimachi, A., Nishi, S., & Ikoma, N. 2014. Robust Color Objects Tracking Method Against Illumination Color Change., 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and IEEE 15th International Symposium on Advanced Intelligent Systems (ISIS). 718-722.

Tao, J., Tan, Y. P., Lu, W. 2007. Robust Color Object Tracking with Application to People Monitoring. International Journal of Image and Graphics. 7(02):227-254.

Hidaka, K. 2009. Robust Color Tracking Based On Mean-Shift

Under Illuminance Change. ICCAS-SICE 2009.1827-1830.

Perez, P., Hue, C., Vermaak J. and Gangnet M.. 2002. Color Based Probabilistic Tracking, ECCV. 1:661-675.

Wren, C., Azarbayejani, A., Darrell, T., & Pentland, A. 1997. Pfinder: Real-Time Tracking of the Human Bod. in IEEE Transactions On Pattern Analysis And Machine Intelligence, 19(7):780-785.

Bogdan, K., 2003. Object Tracking System Based on Color,

Stereovision and Elliptical Shape Features, AVSS

McKenna, S. J., Jabri, S., Duric, Z., Rosenfeld, A., & Wechsler, H.

Tracking Groups of People. Computer Vision and Image Understanding, 80(1):42-56.

Dou, J.-F., and Jian-xun, L. 2014. Robust Visual Tracking Base on Adaptively Multi Feature Fusion and Particle Filter. Optik

International Journal for Light and Electron Optics. 1680-1686.

Comaniciu, D., & Ramesh, V. 2003. U.S. Patent No. 6,590,999. Washington, DC: U.S. Patent and Trademark Office.

Hager, G. D., & Belhumeur, P. N. 1998. Efficient Region Tracking With Parametric Models Of Geometry And Illumination. In IEEE transactions on Pattern Analysis and Machine Intelligence ., 20(10):1025-1039.

Odisio, M., & Bailly, G. 2003. Shape and Appearance Models Of Talking Faces For Model-Based Tracking. En Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop.143-148.

Babenko, B., Yang, M. H., & Belongie, S. 2011. Robust Object Tracking With Online Multiple Instance Learning. IEEE Transactions Pattern Analysis and Machine Intelligence. 33(8):1619-1632.

Bradski, G. Real Time Face and Object Tracking As a Component of a Perceptual User Interface. 4th IEEE Workshop Proceedings

WACV'98. 214-219

Comaniciu, D., & Ramesh, V. 2003. U.S. Patent No. 6,590,999. Washington, DC: U.S. Patent and Trademark Office.Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, Hilton Head Island, SC, 2000, 2:142-149.

Downloads

Published

2017-04-01

How to Cite

Mendoza Chipantasi, D. J., Velasco, N., Rivas, D., & Andaluz, V. H. (2017). Robust Color Tracking to Ambient Light Changes. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 83–87. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1840

Most read articles by the same author(s)