Robust Arm and Hand Tracking by Unsupervised Context Learning

Spruyt, Vincent; Ledda, Alessandro; Philips, Wilfried

doi:10.3390/s140712023

Cited by 9 publications

(5 citation statements)

References 48 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering previous works, we can find some publicly available datasets containing annotated hands [2,3], faces [4] and both [5,6]. The Autonomy [6] dataset stands out among them since it includes new data and combines multiple other datasets with annotated hands and faces that conform to the same annotation standard, totaling 50,365 images.…”

Section: Related Workmentioning

confidence: 99%

Large-scale dataset and benchmarking for hand and face detection focused on sign language

Cavalcante Carneiro,

Pinheiro Salvadeo,

Brito Silva

2023

ESANN 2023 Proceesdings

View full text Add to dashboard Cite

Object detection is an important preprocessing technique for sign language recognition, allowing focus on the most important parts of the image. This paper introduces a new large-scale dataset for hand and face detection in sign language context, mitigating the lack of data for this problem. We evaluated different object detection architectures to find the best trade-off between computational cost and mean Average Precision (mAP). The proposed dataset contains 477,480 annotated images. The most accurate detector (CenterNet) achieved an mAP of 96.7%. Furthermore, the optimizations made to the models reduced the inference time up to 74% in the best scenario.

show abstract

Section: Related Workmentioning

confidence: 99%

Large-scale dataset and benchmarking for hand and face detection focused on sign language

Cavalcante Carneiro,

Pinheiro Salvadeo,

Brito Silva

2023

ESANN 2023 Proceesdings

View full text Add to dashboard Cite

show abstract

“…Furthermore, Liu et al [17] presented a full-body human motion tracking system using exemplar-based conditional particle filter. Similarly, Spruyt et al [18], presented an unsupervised method to automatically learn the context in which a hand was placed. Adams et al [19] used a Kinect sensor and a high-fidelity virtual world interface to acquire the depth image of the scene.…”

Section: Review Of Limb Motion Tracking Systemsmentioning

confidence: 99%

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Akhavizadegan

Idris

Razak

et al. 2020

MJCS

View full text Add to dashboard Cite

Upper limb angular motion measurement is particularly a subject of interest in the field of rehabilitation. It is commonly measured manually by physiotherapist via goniometer to monitor stroke patient’s progress. However, manual measurement has many inherent drawbacks such as the need to have both patient and physiotherapist to be at the same place. In this paper, an automated real-time single-camera upper limb angular motion measuring system is proposed for home-based rehabilitation to aid physiotherapist to monitor patient’s progress. The measurement of concern are angle measurement of elbow extension, elbow flexion, wrist flexion and wrist extension. To do this, we propose a method that utilized predefined coordinate points extracted from the contours of the object named as contour based motion tracking. The proposed method overcome problems of prior target tracking techniques such as Kalman filter, Optical flow and Cam-shift. The proposed method includes skin region segmentation and arm modelling for motion tracking. Prior skin segmentation techniques suffer from fixed threshold value set by the user. Therefore, we introduce dynamic threshold based on the lower and upper threshold boundary of isolated skin regions from the background. To ensure the reliability of our skin segmentation method, we compare them with four different related algorithms. The result shows that our skin segmentation method outperformed the prior method with 93% detection accuracy. Following the segmentation, we model the arm motion tracking by formulating mathematical equation of various points and motion velocities to track the arm. We then model the wrist and elbow position to estimate arm angular motion. The method is put together and tested with real human subject and other test settings. The result shows that our proposed method able to produce an accurate and reliable reading of +-1.25 average range of error from actual physiotherapist reading.

show abstract

“…This goal is tackled with various approaches in the state of the art, using different data inputs and strategies. HT has been extensively applied in a number of fields: gestural interfaces, virtual environments and videogames are only few examples of the areas where it is gradually becoming a key-factor [ 9 , 10 , 11 , 12 ]. Application of HT techniques to help impaired people, including BP, in a number of everyday life problems makes no exception [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Are We Ready to Build a System for Assisting Blind People in Tactile Exploration of Bas-Reliefs?

Carfagni

Furferi

Governi

et al. 2016

Sensors

View full text Add to dashboard Cite

Nowadays, the creation of methodologies and tools for facilitating the 3D reproduction of artworks and, contextually, to make their exploration possible and more meaningful for blind users is becoming increasingly relevant in society. Accordingly, the creation of integrated systems including both tactile media (e.g., bas-reliefs) and interfaces capable of providing the users with an experience cognitively comparable to the one originally envisioned by the artist, may be considered the next step for enhancing artworks exploration. In light of this, the present work provides a description of a first-attempt system designed to aid blind people (BP) in the tactile exploration of bas-reliefs. In detail, consistent hardware layout, comprising a hand-tracking system based on Kinect® sensor and an audio device, together with a number of methodologies, algorithms and information related to physical design are proposed. Moreover, according to experimental test on the developed system related to the device position, some design alternatives are suggested so as to discuss pros and cons.

show abstract

Robust Arm and Hand Tracking by Unsupervised Context Learning

Cited by 9 publications

References 48 publications

Large-scale dataset and benchmarking for hand and face detection focused on sign language

Large-scale dataset and benchmarking for hand and face detection focused on sign language

Real-Time Automated Contour Based Motion Tracking Using a Single-Camera for Upper Limb Angular Motion Measurement

Are We Ready to Build a System for Assisting Blind People in Tactile Exploration of Bas-Reliefs?

Contact Info

Product

Resources

About