Abstract
Hand gesture recognition has become very popular with the advancements in computer vision, and efficient detection of hand gestures is certainly the talk of the hour. It finds numerous worthy real-world applications such as human computer interaction, sign language interpretation, immersive gaming experience, robotics control systems, etc. This paper presents an end-to-end system for the recognition of hand gestures, based on an efficient object detection architecture-RetinaNet. Convolutional Block Attention Module is used to improve performance. The method has been tested on Ouhands dataset and achieved better recognition accuracies and real-time application. Results show that our CBAM-RetinaNet model is robust and efficient in recognition of hand gestures in complex backgrounds and different illumination variations.
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Acknowledgement
We acknowledge the Department of Biotechnology, Government of India for the financial support for the Project BT/COE/34/SP28408/2018.
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Suguna, K.Y., Dutta, H.P.J., Bhuyan, M.K., Laskar, R.H. (2022). Hand Gesture Recognition Using CBAM-RetinaNet. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_38
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