Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Alam, Md. Shahinur; Kwon, Ki‐Chul; Alam, Md. Ashraful; Abbass, Mohammed Y.; Imtiaz, Shariar Md; Kim, Nam

doi:10.3390/s20020376

Cited by 58 publications

(45 citation statements)

References 36 publications

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“…In addition to RGB cameras, 3D camera modules can be used for recognition. The same author also detected trajectories [23] with 99.32% accuracy using LSTM and Convolutional Neural Network (CNN). In the detection and segmentation paradigm, image-based surveillance systems play a significant role.…”

Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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Attention is the mental awareness of human on a particular object or a piece of information. The level of attention indicates how intense the focus is on an object or an instance. In this study, several types of human attention level have been observed. After introducing image segmentation and detection technique for facial features, eyeball movement and gaze estimation were measured. Eye movement were assessed using the video data, and a total of 10197 data instances were manually labelled for the attention level. Then Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based Deep learning (DL) architectures have been proposed for analysing the data. Next, the trained DL model has been implanted into a robotic system that is capable of detecting various features; ultimately leading to the calculation of visual attention for reading, browsing, and writing purposes. This system is capable of checking the attention level of the participants and also can detect if participants are present or not. Based on a certain level of visual focus of attention (VFOA), this system interacts with the person, generates awareness and establishes verbal or visual communication with that person. The proposed ML techniques have achieved almost 99.24% validation accuracy and 99.43% test accuracy. It is also shown in the comparative study that, since the dataset volumes are limited, ANN is more suitable for attention level calculation than RNN-LSTM. We hope that the implemented robotic structure manifests the real-world implication of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Chakraborty¹,

Ahmed²,

Yousuf³

et al. 2021

IEEE Access

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“…Deep learning has become popular for different applications [ 1 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]; single image SR (SISR) is one of them [ 27 , 28 , 29 , 30 , 31 ]. It is a very challenging problem due to the transformation of a specific low-resolution (LR) image to a high-resolution (HR) image.…”

Section: Background Of Iim and Super Resolutionmentioning

confidence: 99%

Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Alam

Erdenebat

Abbass

et al. 2021

Sensors

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The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

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“…For the high dimensional perceptions, MDP states s

S can be utilized by adopting Deep Neural Networks [ 30 ]. Figure 3 speaks to the view of the encompassing inclusion by adopting three vision sensors placed in the front.…”

Section: Distributional Agent For Autonomous Drivingmentioning

confidence: 99%

Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment

Issa

Das

Rahman

et al. 2021

Sensors

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Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

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Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Cited by 58 publications

References 36 publications

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level

Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment

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