A real-time hand gesture recognition method

Zhao, Yafei; Wang, Weidong; Wang, Yuehai

doi:10.1109/icecc.2011.6066597

Cited by 12 publications

(3 citation statements)

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“…Many methods have been proposed for hand gesture recognition in recent years. Zhao et al [11] presented a technique based on computer vision for real-time hand gesture recognition. Adaptive skin color and motion detection is used to identify hand regions.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Rehman¹,

Ahmed²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on visionbased gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.

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Section: Related Workmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Rehman¹,

Ahmed²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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show abstract

“…These features uphold invariance to geometric transformations, excepted object orientation. Recent works in posture recognition appealing to HOG descriptors include [15]. …”

Section: B State-of-the-art Descriptorsmentioning

confidence: 99%

Hand gesture recognition using a dedicated geometric descriptor

Collumeau

Leconge

Emile

et al. 2012

2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)

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A high proportion of hospital-acquired diseases are transmitted nowadays during surgery despite existing asepsis preservation measures. These are quite drastic, prohibiting surgeons from interacting directly with non-sterile equipment. Indirect control is presently achieved through an assistant or a nurse. Gesture-based Human-Computer Interfaces constitue a promising approach for giving direct control over such equipment to surgeons. This paper introduces a novel hand descriptor based on measurements extracted from hand contour convex and concave extrema. Using a 9750-picture database created especially for this purpose, it is compared with three state-ofthe-art description methods, namely Hu moments, and both SIFT and HOG features. Effects of large amounts of hand rotation are also studied on each rotation axis independently. Obtained results give HOG features as best in recognizing hands from our database, closely followed by the proposed descriptor. Performance comparison when facing rotated hands shows our descriptor as the most robust to rotations, outperforming the other descriptors by a wide margin.

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“…Optimizasyon sonucu kullanılmak üzere seçilen parametreler ve bu parametreler ile elde edilen test başarım oranı Tablo 1'de verilmiştir. Yukarıda bahsedilen çalışmaların dışında el jestinin tespitinde YGH yöntemi kullanan çalışmalar da bulunmaktadır [37], [38]. Bu çalışmalarda ortak olarak farklı el jestlerinin tanımlanması işlemi gerçekleştirilmiştir.…”

Section: Klima Durumunun Belirlenmesiunclassified