Abstract
In this article a robust and real-time hand gesture detection and recognition system for dynamic environments is proposed. The system is based on the use of boosted classifiers for the detection of hands and the recognition of gestures, together with the use of skin segmentation and hand tracking procedures. The main novelty of the proposed approach is the use of innovative training techniques - active learning and bootstrap -, which allow obtaining a much better performance than similar boosting-based systems, in terms of detection rate, number of false positives and processing time. In addition, the robustness of the system is increased due to the use of an adaptive skin model, a color-based hand tracking, and a multi-gesture classification tree. The system performance is validated in real video sequences.
Chapter PDF
Similar content being viewed by others
Keywords
References
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Trans. on Pattern Anal. Machine Intell. 25(5), 564–575 (2003)
Liu, X.: Hand gesture recognition using depth data. In: Proc. 6th Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 529–534 (2004)
Kolsch, M., Turk, M.: Robust hand detection. In: Proc. 6th Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 614–619 (2004)
Binh, N.D., Shuichi, E., Ejima, T.: Real-Time Hand Tracking and Gesture Recognition System. In: Proc. GVIP 2005, Cairo, Egypt, pp. 19–21 (2005)
Manresa, C., Varona, J., Mas, R., Perales, F.: Hand Tracking and Gesture Recognition for Human-Computer Interaction. Electronic letters on computer vision and image analysis 5(3), 96–104 (2005)
Fang, Y., Wang, K., Cheng, J., Lu, H.: A Real-Time Hand Gesture Recognition Method. In: Proc. 2007 IEEE Int. Conf. on Multimedia and Expo, pp. 995–998 (2007)
Chen, Q., Georganas, N.D., Petriu, E.M.: Real-time Vision-based Hand Gesture Recognition Using Haar-like Features. In: IMTC 2007. Proc. Instrumentation and Measurement Technology Conf, Warsaw, Poland (2007)
Angelopoulou, A., García-Rodriguez, J., Psarrou, A.: Learning 2D Hand Shapes using the Topology Preserving model GNG. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 313–324. Springer, Heidelberg (2006)
Ong, E.-J., Bowden, R.: A boosted classifier tree for hand shape detection. In: Proc. 6th Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 889–894 (2004)
Wimmer, M., Radig, B.: Adaptive Skin Color Classificator, Int. Journal on Graphics, Vision and Image Processing. Special Issue on Biometrics 2, 39–42 (2006)
Verschae, R., Ruiz-del-Solar, J., Correa, M.: A Unified Learning Framework for object Detection and Classification using Nested Cascades of Boosted Classifiers, Machine Vision and Applications (in press)
Schapire, R.E., Singer, Y.: Improved Boosting Algorithms using Confidence-rated Predictions. Machine Learning 37(3), 297–336 (1999)
Wu, B., Ai, H., Huang, C., Lao, S.: Fast rotation invariant multi-view face detection based on real Adaboost. In: Proc. 6th Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 79–84 (2004)
Abramson, Y., Freund, Y.: Active learning for visual object detection, UCSD Technical Report CS2006-0871 (November 19, 2006)
Fröba, B., Ernst, A.: Face detection with the modified census transform. In: Proc. 6th Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, pp. 91–96 (2004)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Sung, K., Poggio, T.: Example-Based Learning for Viewed-Based Human Face Deteccion. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)
The Gesture Recognition Home Page (August 2007), Available at: http://www.cybernet.com/~ccohen/
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
IDIAP hand gesture database (August 2007), Available at: http://www.idiap.ch/resources/gestures/
RoboCup @Home Official website (August 2007), Available at: http://www.robocupathome.org/
UChile RoboCup Teams official website (August 2007), Available at: http://www.robocup.cl/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Francke, H., Ruiz-del-Solar, J., Verschae, R. (2007). Real-Time Hand Gesture Detection and Recognition Using Boosted Classifiers and Active Learning. In: Mery, D., Rueda, L. (eds) Advances in Image and Video Technology. PSIVT 2007. Lecture Notes in Computer Science, vol 4872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77129-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-540-77129-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77128-9
Online ISBN: 978-3-540-77129-6
eBook Packages: Computer ScienceComputer Science (R0)