A View-Based Real-Time Human Action Recognition System as an Interface for Human Computer Interaction

Choi, Jin Woo; Cho, Yong-Il; Han, Taewoo; Yang, Hyun Seung

doi:10.1007/978-3-540-78566-8_10

Cited by 15 publications

(8 citation statements)

References 4 publications

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“…Content-based video study and intuitive labeling of video clips lead towards improved searching [12]. In human-computer interaction, activity recognition can accommodate towards better natural language understanding that can help us in creating computers with improved speech recognition [13]. Finally, home care technologies can be developed with the ability to identify activities of daily living, decreasing the charges and burdens of care-giving with enhanced care and self-sufficiency in old age [14].…”

Section: A Har Applicationsmentioning

confidence: 99%

Analysis of Deep Neural Networks for Human Activity Recognition in Videos—A Systematic Literature Review

et al. 2021

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From the past few decades, Human activity recognition (HAR) is one of the vital research areas in computer vision in which much research is ongoing. The researcher's focus is shifting towards this area due to its vast range of real-life applications to assist in daily living. Therefore, it is necessary to validate its performance on standard benchmark datasets and state-of-the-art systems before applying it in real-life applications. The primary objective of this Systematic Literature Review (SLR) is to collect existing research on video-based human activity recognition, summarize, and analyze the state-of-the-art deep learning architectures regarding various methodologies, challenges, and issues. The top five scientific databases (such as ACM, IEEE, ScienceDirect, SpringerLink, and Taylor & Francis) are accessed to accompany this systematic study by summarizing 70 different research articles on human activity recognition after critical review. Human activity recognition in videos is a challenging problem due to its diverse and complex nature. For accurate video classification, extraction of both spatial and temporal features from video sequences is essential. Therefore, this SLR focuses on reviewing the recent advancements in stratified self-deriving feature-based deep learning architectures. Furthermore, it explores various deep learning techniques available for HAR, challenges researchers to face to build a robust model, and state-of-theart datasets used for evaluation. This SLR intends to provide a baseline for video-based human activity recognition research while emphasizing several challenges regarding human activity recognition accuracy in video sequences using deep neural architectures.

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Section: A Har Applicationsmentioning

confidence: 99%

Analysis of Deep Neural Networks for Human Activity Recognition in Videos—A Systematic Literature Review

et al. 2021

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“…Human action recognition plays a crucial role in many realworld applications, such as holistic scene understanding, video surveillance, and human-computer interaction (Htike et al 2014;Choi et al 2008). In particular, skeleton-based human action recognition has attracted much attention in recent years and has shown its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

STAR: Sparse Transformer-based Action Recognition

Shi,

Lee,

Qiu

et al. 2021

Preprint

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The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years. However, previous works have mainly focused on over-parameterized and complex models based on dense graph convolution networks, resulting in low efficiency in training and inference. Meanwhile, the Transformer architecture-based model has not yet been well explored for cognitive application in human action and behavior estimation. This work proposes a novel skeleton-based human action recognition model with sparse-attention on the spatial dimension and segmented linear attention on the temporal dimension of data. Our model can also process variable length of video clips grouped as a single batch. Experiments show that our model can achieve comparable performance while utilizing much less trainable parameters and achieve high speed in training and inference. Experiments show that our model achieves 4∼18× speedup and 1 7 ∼ 1 15 model size compared with the baseline models at competitive accuracy.

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“…Video human action recognition (HAR) aims to recognise human actions from videos. It becomes one of the most important tasks in computer vision, driven by a huge range of real-world applications, including human behaviour analysis in surveillance, healthcare, robotics and human-computer interaction, autonomous vehicles, and ambient intelligence [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Current Advances on Deep Learning-based Human Action Recognition from Videos: a Survey

Yi-xiao

Fang

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Human action recognition (HAR) from RGB videos is essential and challenging in the computer vision field due to its wide range of real-world applications in fields of human behaviour analysis, human-computer interactions, robotics and surveillance etc. Since the breakthrough and fast development of deep learning technology, the performance of HAR based on deep neural networks has been significantly improved in this decade. In this survey, we discuss the growing use of deep learning for HAR, such as representative two-stream and 3D CNNs, and particularly highlight most recent success achieved by using attention and transformers. We will provide our perspective on the new trend of designing innovative deep learning methods. In addition, we also present popular HAR datasets developed in recent years and benchmark accuracy achieved by current advancement in deep learning. This draws research attention to the challenges of HAR by identifying performance gaps when applying the deep learning methods on large HAR datasets. Further, this survey sheds light on the development of new methods and facilitates qualitative comparison with state of the art.

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A View-Based Real-Time Human Action Recognition System as an Interface for Human Computer Interaction

Cited by 15 publications

References 4 publications

Analysis of Deep Neural Networks for Human Activity Recognition in Videos—A Systematic Literature Review

Analysis of Deep Neural Networks for Human Activity Recognition in Videos—A Systematic Literature Review

STAR: Sparse Transformer-based Action Recognition

Current Advances on Deep Learning-based Human Action Recognition from Videos: a Survey

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