Robust 3D Hand Detection for Gestures Recognition

Cerlinca, Tudor Ioan; Pentiuc, Štefan Gheorghe

doi:10.1007/978-3-642-24013-3_27

Cited by 15 publications

(11 citation statements)

References 6 publications

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“…The method of hand segmentation mainly uses the depth for the threshold cutting, and it distinguishes the image into foreground and background by setting a certain threshold 57)58) . The Viola-Jones Object-detection Method 59) is an algorithm for image detection of faces, and it is applied to the detection of hand images by modifying some methods. When the images captured by the camera are processed, the result of the processing is usually affected by the noise, so the captured images are usually pre-processed first.…”

Section: Compared With Thementioning

confidence: 99%

[Invited Paper] 3D Display and Interactive Technology in Metaverse

Fan

Lee

et al. 2022

MTA

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The Metaverse is a network of 3D virtual worlds focusing on social connections, and it explores a persistent and decentralized online 3D virtual environment. This virtual environment will allow people to enter the artificial virtual world through 3D display technology, 3D interactive technology, 3D depth sensing technology, virtual reality glasses, augmented reality glasses, mobile phones, personal computers and electronic game consoles 1)2) .The Metaverse includes the physical world and the virtual world. The limitations of the current use of the Metaverse mainly come from the technical limitations of the hardware devices and sensors required to interact with the real-time virtual environment. Among these devices, 3D Display and Interactive Technology plays an important role in the Metaverse. The establishment of a 3D virtual environment originates from 3D display and interactive technology. In response to the coming of the Metaverse Era, this paper will present a detailed discussion of 3D display and interactive technology.The paper consists of six sections. Section 2 reviews the 3D display and depth sensing technology in the literature. Section 3 presents the 3D interactive technology. In Section 4, the immersive experience in Metaverse is presented. Then, the five pillars of the Metaverse is addressed in Section 5. Last, the conclusions are given in Section 6. 3D Display and Depth Sensing TechnologySince the release of the movie "Avatar," 3D movies have sprung up. The world's major home appliance manufacturers have also successively launched a series of televisions (TVs), handheld video games, 3D advertising billboards, and other devices that support stereoscopic display 3) , bringing 3D technology into consumers' lives.These devices equipped with stereoscopic images not only drive the overall development trend of academic research and industry but have also become the trend of the future.

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Section: Compared With Thementioning

confidence: 99%

[Invited Paper] 3D Display and Interactive Technology in Metaverse

Fan

Lee

et al. 2022

MTA

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show abstract

“…It is a portable, noninvasive tool for measuring upper body motion as shown in Figure 4 a,b. There are, in general, two popular ways to track motion and gestures: one is based on computer vision (CV) and the other is based on inertial measurement units (IMU) [ 41 , 42 ]. Despite CV being less-invasive, it has limitations with regard to field of view, occlusion, portability and computational demands [ 43 ].…”

Section: Multimodal Data Collection Platform Designmentioning

confidence: 99%

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)

Zheng

Staubitz

Weitlauf

et al. 2021

Sensors

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Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

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“…The distance considered can be regarding to another part of the user. Cerlinca et al used the head's distance to the sensor as a threshold to obtain the hands, assuming that the hands are always in front of the head and are the closest object to the sensor [6]. Sometimes both skin-color and depth information are used.…”

Section: Related Workmentioning

confidence: 99%

Human-Robot Interaction Based on Gestures for Service Robots

et al. 2017

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Gesture recognition is very important for Human-Robot Interfaces. In this paper, we present a novel depth based method for gesture recognition to improve the interaction of a service robot autonomous shopping cart, mostly used by reduced mobility people. In the proposed solution, the identification of the user is already implemented by the software present on the robot where a bounding box focusing on the user is extracted. Based on the analysis of the depth histogram, the distance from the user to the robot is calculated and the user is segmented using from the background. Then, a region growing algorithm is applied to delete all other objects in the image. We apply again a threshold technique to the original image, to obtain all the objects in front of the user. Intercepting the threshold based segmentation result with the region growing resulting image, we obtain candidate objects to be arms of the user. By applying a labelling algorithm to obtain each object individually, a Principal Component Analysis is computed to each one to obtain its center and orientation. Using that information, we intercept the silhouette of the arm with a line obtaining the upper point of the interception which indicates the hand position. A Kalman filter is then applied to track the hand and based on state machines to describe gestures (Start, Stop, Pause) we perform gesture recognition. We tested the proposed approach in a real case scenario with different users and we obtained an accuracy around 89,7%. 1 Introduction Nowadays, with robots entering in our daily lives, it is becoming important to provide the users a simple and intuitive way to interact with them. Human-Robot interactions has already proved to be a major field in robotics with an

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Robust 3D Hand Detection for Gestures Recognition

Cited by 15 publications

References 6 publications

[Invited Paper] 3D Display and Interactive Technology in Metaverse

[Invited Paper] 3D Display and Interactive Technology in Metaverse

A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)

Human-Robot Interaction Based on Gestures for Service Robots

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