Kinect Based Real Time Gesture Recognition Tool for Air Marshallers and Traffic Policemen

Prakash, Anusha; Swathi, Rotti Srinivasamurthy; Kumar, Stuti; Ashwin, T. S.; Reddy, G. Ram Mohana

doi:10.1109/t4e.2016.015

Cited by 7 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using the following instruction, a visible image of the subject's landmark was obtained. Assume = ( , ) is the correct position of the j th landmark for the person in the image, , 2 . To create a heatmap for this landmark, it is necessary to define a neighborhood to the centrality of this point because it is clear that each heatmap does not contain only one point but also covers a small area.…”

Section: -1-1-groundtruth Translation (Or Annotation Technique)mentioning

confidence: 99%

“…Recent progress in this sensor has provided many opportunities in various fields [1]. This sensor was initially made to control video games, but later, it became a powerful and wellknown tool for scientific research to achieve high-quality depth data [2] quickly. Hence, due to its low price, quick access, ease of use, and decent quality, Kinect has attracted researchers' attention.…”

Section: -Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Depth Dataset Using Microsoft Kinect-v2

Heravi

Delgarmi

et al. 2021

Preprint

View full text Add to dashboard Cite

In biomedical imaging studies, numerous methods have been used to capture human data, mostly by using magnetic resonance imaging (MRI) and computed tomography (CT). However, due to being inexpensive and accessibility of Microsoft Kinect, its usage has been significantly increased in recent years. In this study, we aimed to represent the procedure of data acquisition, which includes a set of depth images from individuals’ back surfaces. The goal of image acquisition is to investigate spinal deformities and landmark detection of the back surface. Traditional imaging systems are challenging, most notably because of ionized beams in the data acquisition process, which has not been solved yet. Indeed, noninvasiveness is the most crucial advantage of our study. Our imaging system was set in a dim laboratory, and the University approved the ethical letter of Medical Sciences before data acquisition. After that, the subjects (total 105; 50 women and 55 men) were recruited, and data images were captured from the back surface. Then, we increased the imaging data size by using the augmentation method to use deep learning methods in future works. Finally, this Dataset leads us to the desired output in our study procedure.

show abstract

Section: -1-1-groundtruth Translation (Or Annotation Technique)mentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

Depth Dataset Using Microsoft Kinect-v2

Heravi

Delgarmi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Further examples from ATC research prototypes combine gestures with eye-tracking [59] or speech recognition [60]. Another application uses visual gesture recognition of air marshallers [61,62].…”

Section: Multimodal Hmis and Their Benefits For Atcmentioning

confidence: 99%

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

et al. 2020

View full text Add to dashboard Cite

Current Air Traffic Controller working positions (CWPs) are reaching their capacity owing to increasing levels of air traffic. The multimodal CWP prototype TriControl combines automatic speech recognition, multitouch gestures, and eye-tracking, aiming for more natural and improved human interaction with air traffic control systems. However, the prototype has not yet undergone systematic evaluation with respect to feasibility. This paper evaluates the operational feasibility, focusing on the system usability of the approach CWP TriControl and its fulfillment of operational requirements. Fourteen controllers took part in a simulation study to evaluate the TriControl concept. The active approach controllers among the group of participants served as the main core target subgroup. The ratings of all controllers in the TriControl assessment were, on average, generally in slight agreement, with just a few showing statistical significance. However, the active approach controllers performed better and rated the system much more positively. The active approach controllers were strongly positive regarding the system usability and acceptance of this early-stage prototype. Particularly, ease of use, user-friendliness, and learnability were perceived very positively. Overall, they were also satisfied with the command input procedure, and would use it for their daily work. Thus, the participating controllers encourage further enhancements to be made to TriControl.

show abstract

“…This section reviews the recent studies conducted in the field of gesture recognition. Moreover, active developments in gesture recognition techniques have led to the recent developments in virtual reality (VR) technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The gesture recognition accuracy, which is a key metric to evaluate the effectiveness of gesture recognition systems, has witnessed steady improvements using input data from multiple sensors [17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…Table 2 represents available joints acquired by each sensor. 11,4 ), Thumb proximal (s 11,5 ), Thumb metacarpal (s 11,6 ) Index distal (s 11,7 ), Index intermediate (s 11,8 ), Index proximal (s 11,9 ), Index metacarpal (s 11,10 ) Middle distal (s 11,11 ), Middle intermediate (s 11,12 ), Middle proximal (s 11,13 ), Middle metacarpal (s 11,14 ) Ring distal (s 11,15 ), Ring intermediate (s 11,16 ), Ring proximal (s 11,17 ), Ring metacarpal (s 11,18 ) Pinky distal (s 11,19 ), Pinky intermediate (s 11,20 ), Pinky proximal (s 11,21 When the gesture recording module records and stores a gesture, the gesture at time t, g t , includes B * t and S t at time t, g t = B * t , S t . Although the proposed method stores S t , S * t ⊂ S t ⊂S, during the gesture registration stage, the learning model learns and recognizes only S * t during the gesture learning and recognition stage in order to reduce the processing time required.…”

Section: Gesture Registration Stagementioning

confidence: 99%

Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

et al. 2019

View full text Add to dashboard Cite

With intelligent big data, a variety of gesture-based recognition systems have been developed to enable intuitive interaction by utilizing machine learning algorithms. Realizing a high gesture recognition accuracy is crucial, and current systems learn extensive gestures in advance to augment their recognition accuracies. However, the process of accurately recognizing gestures relies on identifying and editing numerous gestures collected from the actual end users of the system. This final end-user learning component remains troublesome for most existing gesture recognition systems. This paper proposes a method that facilitates end-user gesture learning and recognition by improving the editing process applied on intelligent big data, which is collected through end-user gestures. The proposed method realizes the recognition of more complex and precise gestures by merging gestures collected from multiple sensors and processing them as a single gesture. To evaluate the proposed method, it was used in a shadow puppet performance that could interact with on-screen animations. An average gesture recognition rate of 90% was achieved in the experimental evaluation, demonstrating the efficacy and intuitiveness of the proposed method for editing visualized learning gestures.

show abstract

Kinect Based Real Time Gesture Recognition Tool for Air Marshallers and Traffic Policemen

Cited by 7 publications

References 5 publications

Depth Dataset Using Microsoft Kinect-v2

Depth Dataset Using Microsoft Kinect-v2

Operational Feasibility Analysis of the Multimodal Controller Working Position “TriControl”

Advanced Machine Learning for Gesture Learning and Recognition Based on Intelligent Big Data of Heterogeneous Sensors

Contact Info

Product

Resources

About