Vision based hand gesture recognition for human computer interaction: a survey

By Siddharth Swarup Rautaray, Anupam Agrawal. Artificial Intelligence Review, 2012

1,521 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/6e33fca1addd62cc278023cabac60141c4af60ec

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In Developmental and Educational Psychology

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Citations in the corpus

Computer Vision and Image Understanding Computer Vision for Assistive Technologies (neutral): “Others common assistive technologies dealing with the improvement of the comfort of person in daily life have been introduced through natural , creative and intuitive methods for communicating and by enabling remote control of devices ( Rautaray and Agrawal, 2015 ).”
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, background): “Since occlusions are one of the most discussed problems in literature [25,26,6 8,82,93,14 9,193] , deep learning allowed to strengthen the detection of single parts in order to ﬁnd and correctly classify occluded pedestrian [82,88,115,116] .”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, neutral): “, control television) based on hand gesture recognition [122], [124] in smart homes share the similar techniques for traffic sign language recognition in smart transportation (Section IV-B) and sign language recognition in smart education (Section IV-D).”
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016, background): “Gesture recognition systems aim to detect and recognize gestures from a limited gesture vocabulary given a sensory input [2].”
EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018, background): “V ISION-BASED gesture recognition [1], [2] is an important and active field of computer vision.”
Location based services: ongoing evolution and research agenda (2018, methodology): “In addition to conventional interaction methods, such as touch, voice-based (B. Lee et al. 2015), gesture-based (Rautaray and Agrawal 2015), and gaze-based methods (i.e. use eye movement to control interaction) (Anagnostopoulos et al. 2017) have been introduced for LBS applications as well.”
Industry use of virtual reality in product design and manufacturing: a survey (2017, background): “…with virtual worlds Numerous publications have reported on novel interaction techniques (Bowman et al. 2001; Pinho et al. 2002; Subramanian and IJsselsteij 2000; Dachselt and Hübner 2007; Rautaray and Agrawal 2015) as well as travel (Morganti et al. 2007; Zanbaka et al. 2004) for virtual reality.”
A review of hand gesture and sign language recognition techniques (2017, background): “These criteria are scalability, robustness, real-time performance and user-independent [57].”
Survey on 3D Hand Gesture Recognition (2016, background): “On the other hand, there are some advantages of vision-based sensors [8]–[10] since they can be”
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors (2017, background): “It can be used widely, including in health monitoring [1][2], smart homes [3][4], and human–computer interactions [5][6]; for example, LSTM cells are a good choice for solving HAR problems.”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “The advancement in speech and vision processing has enabled tremendous research and development in the areas of humancomputer interactions [19], biometric applications [20, 21], security and surveillance [22], and most recently in computational behavioral analysis [23-27].”
Hands Holding Clues for Object Recognition in Teachable Machines (2019, background): “, [29, 45, 54]), as well as within our community [16, 26, 47, 49, 52].”
Vision-based human activity recognition: a survey (2020, methodology): “Some methods of detection based on the form or the appearance, such as colorimetric segmentation, can confuse the human body parts with objects of the scene, as in [118, 145, 222, 223] or cannot operate correctly during variation in appearance or clothing of people such as in [4, 198].”