2,774 research outputs found
Sign Language Fingerspelling Classification from Depth and Color Images using a Deep Belief Network
Automatic sign language recognition is an open problem that has received a
lot of attention recently, not only because of its usefulness to signers, but
also due to the numerous applications a sign classifier can have. In this
article, we present a new feature extraction technique for hand pose
recognition using depth and intensity images captured from a Microsoft Kinect
sensor. We applied our technique to American Sign Language fingerspelling
classification using a Deep Belief Network, for which our feature extraction
technique is tailored. We evaluated our results on a multi-user data set with
two scenarios: one with all known users and one with an unseen user. We
achieved 99% recall and precision on the first, and 77% recall and 79%
precision on the second. Our method is also capable of real-time sign
classification and is adaptive to any environment or lightning intensity.Comment: Published in 2014 Canadian Conference on Computer and Robot Visio
Hand Gesture Recognition for Sign Language Transcription
Sign Language is a language which allows mute people to communicate with other mute or non-mute people. The benefits provided by this language, however, disappear when one of the members of a group does not know Sign Language and a conversation starts using that language. In this document, I present a system that takes advantage of Convolutional Neural Networks to recognize hand letter and number gestures from American Sign Language based on depth images captured by the Kinect camera. In addition, as a byproduct of these research efforts, I collected a new dataset of depth images of American Sign Language letters and numbers, and I compared the presented method for image recognition against a similar dataset but for Vietnamese Sign Language. Finally, I present how this work supports my ideas for the future work on a complete system for Sign Language transcription
Sign Language Recognition using Dynamic Time Warping and Hand Shape Distance Based on Histogram of Oriented Gradient Features
ABSTRACT Recognizing sign language is a very challenging task in computer vision. One of the more popular approaches, Dynamic Time Warping (DTW), utilizes hand trajectory information to compare a query sign with those in a database of examples. In this work, we conducted an American Sign Language (ASL) recognition experiment on Kinect sign data using DTW for sign trajectory similarity and Histogram of Oriented Gradient (HoG
Leveraging 2D pose estimators for American Sign Language Recognition
Most deaf children born to hearing parents do not have continuous access to language, leading to weaker short-term memory compared to deaf children born to deaf parents. This lack of short-term memory has serious consequences on their mental health and employment rate. To this end, prior work has explored CopyCat, a game where children interact with virtual actors using sign language. While CopyCat has been shown to improve language generation, reception, and repetition, it uses expensive hardware for sign language recognition. This thesis explores the feasibility of using 2D off-the-shelf camera-based pose estimators such as MediaPipe for complementing sign language recognition and moving towards a ubiquitous recognition framework. We compare MediaPipe with 3D pose estimators such as Azure Kinect to determine the feasibility of using off-the-shelf cameras. Furthermore, we develop and compare Hidden Markov Models (HMMs) with state-of-the-art recognition models like Transformers to determine which model is best suited for American Sign Language Recognition in a constrained environment. We find that MediaPipe marginally outperforms Kinect in various experimental settings. Additionally, HMMs outperform Transformers by on average 17.0% on recognition accuracy. Given these results, we believe that a widely deployable game using only a 2D camera is feasible.Undergraduat
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
RGBD Datasets: Past, Present and Future
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style
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