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Real-time Hand Tracking for Dynamic Gesture Recognition

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 336)

Abstract

In the recent year, gesture recognition has become the most intuitive and effective communication technique for human interaction with machines. In this paper, we are going to work on hand gesture recognition and interpret the meaning of it from video sequences. Our work takes place in the following three phases: (1) hand detection and tracking, (2) feature extraction, and (3) gesture recognition. We have started proposed work with first step as applying hand tracking and hand detection algorithm to track hand motion and to extract position of the hand. Trajectory-based features are being drawn out from hand and used for recognition process, and hidden Markov model is being designed for each gesture for gesture recognition. Hidden Markov Model is basically a powerful statistical tool to model generative sequences. Our method is being tested on our own data set of 16 gestures, and the average recognition rate we have got is 91 %. With proposed methodology gives the better recognition results compare with the traditional approaches such as PCA, ANN, SVM, and DTW.

Keywords

  • Haar cascade
  • Adaboost algorithm
  • Hidden Markov model
  • Douglas–Peucker algorithm

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Correspondence to Varsha Dixit .

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Dixit, V., Agrawal, A. (2015). Real-time Hand Tracking for Dynamic Gesture Recognition. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_12

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  • DOI: https://doi.org/10.1007/978-81-322-2220-0_12

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