(2011-2013) – University of Pernambuco – Brazil
Gesture recognition became an important research field in the last years, due to the easy access to different vision sensors, the development of computer vision techniques and the necessity of natural cues for human-machine interaction. Among the solutions for gesture recognition, the use of common video cameras provides a non-invasive and easy to implement the method. This raises one problem: how to represent dynamic gestures in a way that computer models can recognize them with a high generalization capability.
The outcome of this project was a novel technique for hand-gesture description, based on salient points. The technique was evaluated for the tasks of gesture recognition and prediction, using different machine learning methods and showed to be competitive with state-of-the-art solutions.
Data & Code
Publications associated with this project
- Barros, P., Maciel-Junior, N. T., Fernandes, B. J., Bezerra, B. L., & Fernandes, S. M. (2017). A dynamic gesture recognition and prediction system using the convexity approach. Computer Vision and Image Understanding, 155, 139-149.
- Barros, P. V., Júnior, N. T., Bisneto, J. M., Fernandes, B. J., Bezerra, B. L., & Fernandes, S. M. (2013, September). An effective dynamic gesture recognition system based on the feature vector reduction for SURF and LCS. In International Conference on Artificial Neural Networks (pp. 412-419). Springer, Berlin, Heidelberg.
- Junior, N. T., Barros, P. V., Fernandes, B. J., Bezerra, B. L., & Fernandes, S. M. (2013, September). A Dynamic Gesture Prediction System Based on the CLCS Feature Extraction. In Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on (pp. 501-506). IEEE.
- Barros, P. V., Junior, N., Bisneto, J. M., Fernandes, B. J., Bezerra, B. L., & Fernandes, S. M. (2013, March). Convexity local contour sequences for gesture recognition. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (pp. 34-39). ACM.