Abstract
Hand gestures are becoming more and more efficient and intuitive means of communication between human and machine. While many proposed methods aim at increasing performance of recognition from spotted gestures segments, it lacks efficient solutions for both detection and recognition gesture from continuous video streams for practical application. In this paper, we approach by using a simple CNN detector to detect gesture candidates and a more precise and complicated CNN classifier to recognize gesture categories. We first deploy a method recently proposed in [5]. However, we improve that method by adjusting another condition for gesture decision making to avoid detection missing due to the detector performance. Our improved algorithm is compared with the original one, showing an improvement in term of overall accuracy (from 73.9% to 79.3%) on the same dataset.
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-20-1-4053.
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Tran, TH., Do, VH. (2021). Improving Continuous Hand Gesture Detection and Recognition from Depth Using Convolutional Neural Networks. In: Tran, DT., Jeon, G., Nguyen, T.D.L., Lu, J., Xuan, TD. (eds) Intelligent Systems and Networks . ICISN 2021. Lecture Notes in Networks and Systems, vol 243. Springer, Singapore. https://doi.org/10.1007/978-981-16-2094-2_10
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DOI: https://doi.org/10.1007/978-981-16-2094-2_10
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