Abstract
In this paper we address the problem of real time object tracking in complex scenes under dynamically changing lighting conditions. This problem affects video-surveillance applications where object location must be known at any time. We are interested in locating and tracking people in video sequences for access control and advanced user interface applications. Here we present a real time tracking method suitable for human faces. A Skin Probability Image (SPI) is generated by applying a skin hue model to the input frame. Targets are located by applying a modified mean-shift algorithm. To obtain their spatial extent, error ellipses are fitted to the probability distributions representing them. The hue model is unique for each target and it is updated each frame to cope with lighting variations. This technique has been applied to human face tracking in indoor environments to test its performance in different situations.
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References
Yang, J., Waibel, A.: Tracking Human Faces in Real-Time. CMU-CS-95-210 (1995)
Störring, M., Andersen, G.E.: Skin Colour Detection under Changing Lighting Conditions. In: 7th Symposium on Intelligent Robotics Systems (1999)
Raja, Y., McKena, S., Gong, S.: Tracking and Segmenting People in Varying Lighting Conditions using Colour. In: Proceedings of FG 1998, Nara, Japan, April 14-16 (1998)
Hsu, R., Mottaleb, M.A., Jain, A.K.: Face Detection in Color Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 696–706 (1998)
Orrite, C., Bernués, E., Gracia, J., Gil, I.: Face detection and recognition in a video sequence. In: Proceedings of SPIE, Orlando (USA), April 2004. Biometric Technology for Human Identification, vol. 5404 (2004)
Bradsky, G.R.: Computer Vision Face Tracking for Use in a Perceptual User Interface. Intel Technology Journal Q2 (1998)
Nait-Charif, H., McKenna, S.: Head Tracking and Action Recognition in a Smart Meeting Room. In: Proceedings 4th IEEE International Workshop on PETS, Graz, Austria (2003)
Kalman, R.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering 82, D. 35–45 (1960)
Bourgin, D.: Color Space FAQ, www.neuro.sfc.keio.ac.jp/~aly/polygon/info/colorspace-faq.html
Comaniciu, D., Armes, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects using Mean Shift. In: IEEE CVPR (2000)
Cheng, Y.: Mean Shift, Mode Seeking and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 190–799 (1995)
Ribeiro, M.I.: Gaussian Probability Density Functions: Properties and Error Characterization. Institute for Systems and Robotics. Lisboa Portugal, www.omni.isr.ist.utl.pt/~mir/pub/probability.pdf
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© 2005 Springer-Verlag Berlin Heidelberg
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Gracia-Roche, J.J., Orrite, C., Bernués, E., Herrero, J.E. (2005). Color Distribution Tracking for Facial Analysis. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_59
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DOI: https://doi.org/10.1007/11492429_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26153-7
Online ISBN: 978-3-540-32237-5
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