ICTACT Journal on Image and Video Processing

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Smart Gesture using Real Time Object Tracking


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1 Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, India
     

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Gesture can be used to interact with the computer without any physical contact. The use of keyboard and mouse can be minimized. Gesture can be of various types. One such type is movement of hand in a particular posture. To detect these type of gestures first it must be verified that the hand is present in frame and is present in the required posture. The first one is achieved by creating a mask of the frame considering the skin color range in the HSV color space. The later part involves shape matching with some template shape. The shape matching involves computing of central moments between the mask and the template shape. The hand posture defines the start and end of gesture. All the movement of hand between start and end of gesture is tracked and gesture is recognized from the tracked data. For the purpose of recognition, Convolution Neural Network is used. An application is built on recognition. Once a gesture is recognized an event will be triggered.

Keywords

Contour Detection, Shape Matching, Hue Moments, Convolution Neural Network, Event Triggering.
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  • Z. Yang, Y. Li, W. Chen and Y. Zheng, “Dynamic Hand Gesture Recognition using Hidden Markov Models”, Proceedings of 7th International Conference on Computer Science and Education, pp. 360-365, 2012.

  • C. Wang, Z. Liu and S. Chan, “Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera”, IEEE Transactions on Multimedia, Vol. 17, No. 1, pp. 29-39, 2015.

  • M. Panwar and P. Singh Mehra, “Hand Gesture Recognition for Human Computer Interaction”, Proceedings of International Conference on Image Information Processing, pp. 1-7, 2011.

  • P. Xu, “A Real-Time Hand Gesture Recognition and Human-Computer Interaction System”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2017.

  • R. Parashar and R. Pareek, “Event Triggering using Hand Gesture using OpenCV”, International Journal of Engineering and Computer Science, Vol. 5, No. 2, pp. 57-60, 2016.

  • K.B. Shaik, P. Ganesan, V. Kalist, B. Sathish and J.M.M. Jenitha, “Comparative Study of Skin Color Detection and Segmentation in HSV and YCBCR Color Space”, Procedia Computer Science, Vol. 57, pp. 41-48, 2015.

  • G. Bradski, “The OpenCV Library”, Dr. Dobb’s Journal of Software Tools, Vol. 120, pp. 122-125, 2000.

  • J. Brownlee, “Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras”, Available at: https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/

  • S. Dey, “CNN Application on Structured Data-Automated Feature Extraction”, Available at: https://towardsdatascience.com/cnn-application-on-structured-data-automated-feature-extraction-8f2cd28d9a7e

  • OpenCV 4.1.0, Available at: https://opencv.org/releases/

  • Keras Documentation, “Conv1D”, Available at: https://keras.io/layers/convolutional.


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  • Smart Gesture using Real Time Object Tracking

Abstract Views: 307  |  PDF Views: 0

Authors

Sumanth Bhat
Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, India
N. Lavanya
Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, India
M. A. Anusuya
Department of Computer Science and Engineering, Sri Jayachamarajendra College of Engineering, India

Abstract


Gesture can be used to interact with the computer without any physical contact. The use of keyboard and mouse can be minimized. Gesture can be of various types. One such type is movement of hand in a particular posture. To detect these type of gestures first it must be verified that the hand is present in frame and is present in the required posture. The first one is achieved by creating a mask of the frame considering the skin color range in the HSV color space. The later part involves shape matching with some template shape. The shape matching involves computing of central moments between the mask and the template shape. The hand posture defines the start and end of gesture. All the movement of hand between start and end of gesture is tracked and gesture is recognized from the tracked data. For the purpose of recognition, Convolution Neural Network is used. An application is built on recognition. Once a gesture is recognized an event will be triggered.

Keywords


Contour Detection, Shape Matching, Hue Moments, Convolution Neural Network, Event Triggering.

References