24,865 research outputs found

    Automatic Sign Language Recognition from Image Data

    Get PDF
    Tato práce se zabývá problematikou automatického rozpoznávání znakového jazyka z obrazových dat. Práce představuje pět hlavních přínosů v oblasti tvorby systému pro rozpoznávání, tvorby korpusů, extrakci příznaků z rukou a obličeje s využitím metod pro sledování pozice a pohybu rukou (tracking) a modelování znaků s využitím menších fonetických jednotek (sub-units). Metody využité v rozpoznávacím systému byly využity i k tvorbě vyhledávacího nástroje "search by example", který dokáže vyhledávat ve videozáznamech podle obrázku ruky. Navržený systém pro automatické rozpoznávání znakového jazyka je založen na statistickém přístupu s využitím skrytých Markovových modelů, obsahuje moduly pro analýzu video dat, modelování znaků a dekódování. Systém je schopen rozpoznávat jak izolované, tak spojité promluvy. Veškeré experimenty a vyhodnocení byly provedeny s vlastními korpusy UWB-06-SLR-A a UWB-07-SLR-P, první z nich obsahuje 25 znaků, druhý 378. Základní extrakce příznaků z video dat byla provedena na nízkoúrovňových popisech obrazu. Lepších výsledků bylo dosaženo s příznaky získaných z popisů vyšší úrovně porozumění obsahu v obraze, které využívají sledování pozice rukou a metodu pro segmentaci rukou v době překryvu s obličejem. Navíc, využitá metoda dokáže interpolovat obrazy s obličejem v době překryvu a umožňuje tak využít metody pro extrakci příznaků z obličeje, které by během překryvu nefungovaly, jako např. metoda active appearance models (AAM). Bylo porovnáno několik různých metod pro extrakci příznaků z rukou, jako např. local binary patterns (LBP), histogram of oriented gradients (HOG), vysokoúrovnové lingvistické příznaky a nové navržená metoda hand shape radial distance function (hRDF). Bylo také zkoumáno využití menších fonetických jednotek, než jsou celé znaky, tzv. sub-units. Pro první krok tvorby těchto jednotek byl navržen iterativní algoritmus, který tyto jednotky automaticky vytváří analýzou existujících dat. Bylo ukázáno, že tento koncept je vhodný pro modelování a rozpoznávání znaků. Kromě systému pro rozpoznávání je v práci navržen a představen systém "search by example", který funguje jako vyhledávací systém pro videa se záznamy znakového jazyka a může být využit například v online slovnících znakového jazyka, kde je v současné době složité či nemožné v takovýchto datech vyhledávat. Tento nástroj využívá metody, které byly použity v rozpoznávacím systému. Výstupem tohoto vyhledávacího nástroje je seřazený seznam videí, které obsahují stejný nebo podobný tvar ruky, které zadal uživatel, např. přes webkameru.Katedra kybernetikyObhájenoThis thesis addresses several issues of automatic sign language recognition, namely the creation of vision based sign language recognition framework, sign language corpora creation, feature extraction, making use of novel hand tracking with face occlusion handling, data-driven creation of sub-units and "search by example" tool for searching in sign language corpora using hand images as a search query. The proposed sign language recognition framework, based on statistical approach incorporating hidden Markov models (HMM), consists of video analysis, sign modeling and decoding modules. The framework is able to recognize both isolated signs and continuous utterances from video data. All experiments and evaluations were performed on two own corpora, UWB-06-SLR-A and UWB-07-SLR-P, the first containing 25 signs and second 378. As a baseline feature descriptors, low level image features are used. It is shown that better performance is gained by higher level features that employ hand tracking, which resolve occlusions of hands and face. As a side effect, the occlusion handling method interpolates face area in the frames during the occlusion and allows to use face feature descriptors that fail in such a case, for instance features extracted from active appearance models (AAM) tracker. Several state-of-the-art appearance-based feature descriptors were compared for tracked hands, such as local binary patterns (LBP), histogram of oriented gradients (HOG), high-level linguistic features or newly proposed hand shape radial distance function (denoted as hRDF) that enhances the feature description of hand-shape like concave regions. The concept of sub-units, that uses HMM models based on linguistic units smaller than whole sign and covers inner structures of the signs, was investigated in the proposed iterative method that is a first required step for data-driven construction of sub-units, and shows that such a concept is suitable for sign modeling and recognition tasks. Except of experiments in the sign language recognition, additional tool \textit{search by example} was created and evaluated. This tool is a search engine for sign language videos. Such a system can be incorporated into an online sign language dictionary where it is difficult to search in the sign language data. This proposed tool employs several methods which were examined in the sign language recognition task and allows to search in the video corpora based on an user-given query that consists of one or multiple images of hands. As a result, an ordered list of videos that contain the same or similar hand configurations is returned

    Gesture and sign language recognition with deep learning

    Get PDF

    Toward a Motor Theory of Sign Language Perception

    Get PDF
    Researches on signed languages still strongly dissociate lin- guistic issues related on phonological and phonetic aspects, and gesture studies for recognition and synthesis purposes. This paper focuses on the imbrication of motion and meaning for the analysis, synthesis and evaluation of sign language gestures. We discuss the relevance and interest of a motor theory of perception in sign language communication. According to this theory, we consider that linguistic knowledge is mapped on sensory-motor processes, and propose a methodology based on the principle of a synthesis-by-analysis approach, guided by an evaluation process that aims to validate some hypothesis and concepts of this theory. Examples from existing studies illustrate the di erent concepts and provide avenues for future work.Comment: 12 pages Partiellement financ\'e par le projet ANR SignCo

    Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation

    Get PDF
    Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner. This is achieved by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture. This joint approach does not require any ground-truth timing information, simultaneously solving two co-dependant sequence-to-sequence learning problems and leads to significant performance gains. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models, in some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We also share new baseline translation results using transformer networks for several other text-to-text sign language translation tasks

    Progressive Transformers for End-to-End Sign Language Production

    Get PDF
    The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need for architectures that are better suited to the continuous domain of full sign sequences. In this paper, we propose Progressive Transformers, a novel architecture that can translate from discrete spoken language sentences to continuous 3D skeleton pose outputs representing sign language. We present two model configurations, an end-to-end network that produces sign direct from text and a stacked network that utilises a gloss intermediary. Our transformer network architecture introduces a counter that enables continuous sequence generation at training and inference. We also provide several data augmentation processes to overcome the problem of drift and improve the performance of SLP models. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging RWTH-PHOENIX-Weather-2014T(PHOENIX14T) dataset and setting baselines for future research

    A Comprehensive Approach to Automated Sign Language Translation

    Get PDF
    Many sign languages are bonafide natural languages with grammatical rules and lexicons, hence can benefit from neural machine translation methods. As significant advances are being made in natural language processing (specifically neural machine translation) and in computer vision processes, specifically image and video captioning, related methods can be further researched to boost automated sign language understanding. This is an especially challenging AI research area due to the involvement of a continuous visual-spatial modality, where meaning is often derived from context. To this end, this thesis is focused on the study and development of new computational methods and training mechanisms to enhance sign language translation in two directions, signs to texts and texts to signs. This work introduces a new, realistic phrase-level American Sign Language dataset (ASL/ ASLing), and investigates the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating ASL to spoken American English. Additionally, the research considers the role of multiple features for improved translation, via various fusion architectures. As an added benefit, with continuous sign language being challenging to segment, this work also explores the use of overlapping scaled visual segments, across the video, for simultaneously segmenting and translating signs. Finally, a quintessential interpreting agent not only understands sign language and translates to text, but also understands the text and translates to signs. Hence, to facilitate two-way sign language communication, i.e. visual sign to spoken language translation and spoken to visual sign language translation, a dual neural machine translation model, SignNet, is presented. Various training paradigms are investigated for improved translation, using SignNet. By exploiting the notion of similarity (and dissimilarity) of visual signs, a metric embedding learning process proved most useful in training SignNet. The resulting processes outperformed their state-of-the-art counterparts by showing noteworthy improvements in BLEU 1 - BLEU 4 scores
    corecore