A Vision-Based Human Action Recognition System for Companion Robots and Human Interaction

Chiang, Meng-Lin; Feng, Jian-Kai; Zeng, Wen-Lin; Fang, Chiung‐Yao; Chen, Sei-Wang

doi:10.1109/compcomm.2018.8780777

Cited by 13 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning (CNNs and RNNs) addresses the critical task of human action recognition in computer vision, enhancing accuracy and optimizing performance. [9,[61][62][63][64][65][66][67][68][69][70][71][72] Attention-based LSTM for feature distinctions, incorporating a spatiotemporal saliency-based multi-stream network. [73] A hybrid deep learning model for human action recognition.…”

Section: Methods Referencesmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Manakitsa,

Maraslidis,

Moysis

et al. 2024

Technologies

View full text Add to dashboard Cite

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.

show abstract

Section: Methods Referencesmentioning

confidence: 99%

“…The fusion of modalities like RGB and depth information further refines recognition. Recent strides in attention mechanisms and metaheuristic algorithms have optimized network architectures, emphasizing relevant regions for improved performance [9,[61][62][63][64][65][66][67][68][69][70][71][72].…”

Section: Human Action Recognitionmentioning

confidence: 99%

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Manakitsa,

Maraslidis,

Moysis

et al. 2024

Technologies

View full text Add to dashboard Cite

show abstract

“…More specifically, in [52], [53], it was shown that higher recognition accuracies were reached when fusing RGB video and inertial sensing. Similarly, in [20], [54]- [58], higher recognition accuracies were obtained when fusing RGB video and depth sensing. In [59], [60], RGB, depth and inertial signals were simultaneously used to achieve higher recognition accuracies.…”

Section:  Introductionmentioning

confidence: 93%

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Majumder

Kehtarnavaz

2021

IEEE Sensors J.

View full text Add to dashboard Cite

Human action recognition is used in many applications such as video surveillance, human-computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This paper provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.

show abstract

Hybrid Technique for Human Activities and Actions Recognition Using PCA, Voting, and K-means

Sekhon¹,

Singh²

2022

International Conference on Innovative Computing and Communications

View full text Add to dashboard Cite

A Vision-Based Human Action Recognition System for Companion Robots and Human Interaction

Cited by 13 publications

References 6 publications

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Hybrid Technique for Human Activities and Actions Recognition Using PCA, Voting, and K-means

Contact Info

Product

Resources

About