Video action classification using symmelets and deep learning

Alghyaline, Salah; Hsieh, Jun-Wei; Chuang, Chi-Hung

doi:10.1109/smc.2017.8122640

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…During the last decade, Convolutional Neural Networks (CNNs) features [74][75][76][77] achieved stateof-the-art results in object detection compared with handcrafted features. The deep network includes many layers.…”

Section: Learned Featuresmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

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Section: Learned Featuresmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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“…The highlight of the method is that the preprocessing time is reduced to one-sixth, and the training time is reduced to one-third of the time it would normally take. For action content to be presented more accurately, the authors in [1] divided using the symmetrical properties often in various video scenes to lter out redundant (or background) features on two datasets, HMDB51 and UCF101. In another study [38], scientists proposed a humanrelated multi-stream CNN (HR-MSCNN) architecture, combining traditional streams with novel human-related streams.…”

Section: Deep Learning Human Action Recognitionmentioning

confidence: 99%

Taekwondo Pose Estimation With Deep Learning Architectures on One-Dimensional and Two-Dimensional Data

Nguyen,

Ha,

Hoang

et al. 2023

JCC

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Practicing sports is an activity that helps people maintain and improve their health, enhance memory and concentration, reduce anxiety and stress, and train teamwork and leadership ability. With the development of science and technology, artificial intelligence in sports has become increasingly popular with the public and brings many benefits. In particular, many applications help people track and evaluate athletes' achievements in competitions. This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Long-term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second, we use sequence images to train using video classification architecture. Finally, we recognize poses in sports lessons using skeleton data to remove noise in the image, such as background and extraneous objects behind the exerciser. As a result, our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson.

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“…Another ConvNets based video classification was proposed to recognize human actions based on motion sequence information in RGB-D video using DL by ljjina and Chalavadi [163]. Alghyaline et al [18] proposed a novel video representation to improve human action recognition based on symmelets, IDT and DL. Parisi et al [164] introduce a deep neural architecture for the lifelong learning of the body.…”

Section: Voice and Video Processingmentioning

confidence: 99%

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

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Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma, konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır. Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında bilgiler vererek araştırmacılara rehberlik etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat çekerek olası araştırma konularını listeliyoruz. Günümüzde, Derin öğrenme yöntemleri nesne tanıma alanında insanlardan daha iyi sonuçlara ulaşmıştır. DL üzerine yapılan çalışmalar dikkate alındığında, bu başarının otonom araçlar, tıbbi görüntü işleme, büyük veri analizi ve karakter tanıma gibi alanlarda da gerçekleşeceği öngörülmektedir. https://dx.Deep learning (DL) is an important machine learning field that has achieved considerable success in many research areas. In the last decade, the-state-of-the-art studies on many research areas such as computer vision, object recognition, speech recognition, and natural language processing were especially led to the awakening of the artificial intelligence from deep sleep. Nowadays, many researchers try to find solutions to many problems in various fields under the light of DL methods. In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers. This study investigated DL studies which are made in the most popular and challenging fields such as autonomous vehicles, natural language processing, handwritten character recognition, signature verification, voice and video recognition, medical image processing, and big data. Furthermore, this study points out the remaining challenges of these research areas that can be solved by DL, and discusses future topics to help the researchers. In the present day, Deep learning methods have reached better results than humans in object recognition. According to the literature studies on DL, It is foreseen that this success will be achieved in areas such as autonomous vehicles, medical image processing, big data analysis, and character recognition. https://dx.

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Video action classification using symmelets and deep learning

Cited by 6 publications

References 21 publications

Arabic Optical Character Recognition: A Review

Arabic Optical Character Recognition: A Review

Taekwondo Pose Estimation With Deep Learning Architectures on One-Dimensional and Two-Dimensional Data

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

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