Robust Modelling of Static Hand Gestures using Deep Convolutional Network for Sign Language Translation

Singh, Dushyant Kumar; Kumar, Anshu; Ansari, M. A.

doi:10.1109/icccis51004.2021.9397203

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…After the great sensation of 2DCNN, 3DCNN [7,18] has also excelled in video processing tasks. 3DCNN works in single-stage pipelining, where feature extraction and classification tasks are performed in a single module.…”

Section: Advanced Three-dimensional Convolutional Network (A3dconvnet)mentioning

confidence: 99%

“…Ruchi and Manish [17] use Xception and LSTM networks for temporal sequencing analysis. Ansari and Singh [18] also take advantage of InceptionV3 and LSTM modules to do the same. Our method uses a deep structure of 3DCNN to build a spatiotemporal relationship on the segmented forepart of the video sequences and identify abnormal person movements.…”

Section: Metricsmentioning

confidence: 99%

“…This is because so far many such incidents have come to the fore in stores/malls, due to which the business has suffered a lot. The National Retail Federation (NRF) [18] also sees theft as a severe problem in retail shrinkage. Therefore, this research focuses on detecting the abnormal acts that can harm the retailers in the business.…”

Section: Introductionmentioning

confidence: 99%

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Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Ansari

Singh

2023

Journal of Electrical Engineering

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The use of neural networks in a range of academic and scientific pursuits has introduced a great interest in modeling human behavior and activity patterns to recognize particular events. Various methods have so far been proposed for building expert vision systems to understand the scene and draw true semantic inferences from the observed dynamics. However, classifying abnormal or unusual activities in real-time video sequences is still challenging, as the details in video sequences have a time continuity constraint. A cost-effective approach is still demanding and so this work presents an advanced three-dimensional convolutional network (A3DConvNet) for detecting abnormal behavior of persons by analyzing their actions. The network proposed is 15 layers deep that uses 18 convolutional operations to effectively analyze the video contents and produces spatiotemporal features. The integrated dense layer uses these features for the efficient learning process and the softmax layer is used as the output layer for labeling the sequences. Additionally, we have created a dataset that carries video clips to represent abnormal behaviors of humans in megastores/shops, which is a consequent contribution of this paper. The dataset includes five complicated activities in the shops/megastores: normal, shoplifting, drinking, eating, and damaging. By analyzing human actions, the proposed algorithm produces an alert if anything like abnormalities is found. The extensive experiments performed on the synthesized dataset demonstrate the effectiveness of our method, with achieved accuracy of up to 90.90%.

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Section: Advanced Three-dimensional Convolutional Network (A3dconvnet)mentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

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Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Ansari

Singh

2023

Journal of Electrical Engineering

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Machine translation from signed to spoken languages: state of the art and challenges

Coster

Shterionov

Herreweghe

et al. 2023

Univ Access Inf Soc

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Automatic translation from signed to spoken languages is an interdisciplinary research domain on the intersection of computer vision, machine translation (MT), and linguistics. While the domain is growing in terms of popularity—the majority of scientific papers on sign language (SL) translation have been published in the past five years—research in this domain is performed mostly by computer scientists in isolation. This article presents an extensive and cross-domain overview of the work on SL translation. We first give a high level introduction to SL linguistics and MT to illustrate the requirements of automatic SL translation. Then, we present a systematic literature review of the state of the art in the domain. Finally, we outline important challenges for future research. We find that significant advances have been made on the shoulders of spoken language MT research. However, current approaches often lack linguistic motivation or are not adapted to the different characteristics of SLs. We explore challenges related to the representation of SL data, the collection of datasets and the evaluation of SL translation models. We advocate for interdisciplinary research and for grounding future research in linguistic analysis of SLs. Furthermore, the inclusion of deaf and hearing end users of SL translation applications in use case identification, data collection, and evaluation, is of utmost importance in the creation of useful SL translation models.

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Detecting abnormal behavior in megastore for crime prevention using a deep neural architecture

Ansari

Singh

2023

Int J Multimed Info Retr

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Robust Modelling of Static Hand Gestures using Deep Convolutional Network for Sign Language Translation

Cited by 4 publications

References 13 publications

Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Machine translation from signed to spoken languages: state of the art and challenges

Detecting abnormal behavior in megastore for crime prevention using a deep neural architecture

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