DETECTION AND RECOGNITION OF STATIC AND DYNAMIC HAND GESTURES
DOI:
https://doi.org/10.24867/03SA01MarkovicKeywords:
Handgesture recognition, machine learning, convolutional neural networksAbstract
In this paper, the process of recognizing hand gestures based on RGB images and videos is described, where the idea was to explore possibilities of detection arm and hand position and hand gestures. Methods that were developed and analyzed in this paper are based on machine learning, but also along them, some methods that aren't based on machine learning were also analyzed, to compere results. First, the method of static hand gestures recognition using already existing OpenCV library methods were described. Next, the method for detecting static hand gestures using convolutional neural networks was developed. Finally, as the main focus of the paper, the method of detecting dynamic hand gestures was implemented [1], using machine learning based on 3D convolutional neural networks. This method was further adapted to optimize the results of a selected data set. Likewise, experimental results are given, where the performances of the implemented algorithm was analyzed.
References
[2] E. Ohn-Bar and M. M. Trivedi, “Hand Gesture Recognition in Real Time for Automotive Interfaces: A Multimodal Vision-Based Approach and Evaluations”,IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 6, 2014.
[3] Y. Zhu and B. Yuan, “Real-time hand gesture recognition with Kinect for playing racing video games”,International Joint Conference on Neural Networks (IJCNN), pp. 3240-3246., 2014.
[4] T. Starner, J. Weaver and A. Pentland, “Real-time American sign language recognition using desk and wearable computer based video”,IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 12, pp. 1371-1375, 1998.
[5] S. Loehmann, M. Knobel, M. Lamara, A. Butz, “Culturally Independent Gestures for in Car Interactionsm”,Kotzé P. et al. (eds) Human-Computer Interaction – INTERACT 2013. Lecture Notes in Computer Science, vol 8119., 2013.
[6]P. Molchanov, S. Gupta, K. Kim and K. Pulli, “Multi-sensor system for driver's hand-gesture recognition”,11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-8.,2015.
[7]J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, L. Wang, G. Wang, J. Cai, T. Chen,“Recent Advances in Convolutional Neural”, arXiv:1512.07108, 2017.
[8] S. U. Rahman, Z. Afroze, M. Tareq, “Hand Gesture Recognition Techniques For Human Computer Interaction Using OpenCv”, International Journal of Scientific and Research Publications, vol. 4, no. 12, 2014.
[9]Y. Hsiao, J. Sanchez-Riera, T. Lim, K. Hua, W. Cheng, “LaRED: A Large RGB-D Extensible Hand Gesture Dataset”, The 2014 ACM Multimedia Systems Conference, 2014.
