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An intelligent system control method based on visual sensor

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Title: An intelligent system control method based on visual sensor
Authors: Haijun Diao, Lina Yin, Bin Liang, Yanyan Chen
Source: Measurement: Sensors, Vol 29, Iss , Pp 100857- (2023)
Publisher Information: Elsevier, 2023.
Publication Year: 2023
Collection: LCC:Electric apparatus and materials. Electric circuits. Electric networks
Subject Terms: Visual sensors, Intelligent systems, Convolutional neural network, Video representation learning, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
More Details: In order to solve the complexity problem caused by the uncertainty of control system models, this paper utilizes visual sensors and intelligent control technology, and uses data-driven machine learning algorithms to extract representations from the original video for video representation learning, providing crucial semantic features for related tasks. Convolutional Neural Network (CNN) greatly improves the utilization efficiency of visual data and model performance, and realizes the recognition of complex application scenarios. In this paper, an intelligent system control method of time-domain vision sensor is proposed. The proposed method locate, track and measure the speed of moving objects based on CNN and image acquisition device, Asynchronous Temporal Vision Sensor (ATVS). The experimental results show that our proposed algorithm has improved its overall performance through video feature learning and clustering. It not only pays more attention to video spatial information to enhance the discrimination ability of learned video representations, such as scenes and objects, but also improves the tracking performance of visual sensors under various interference attributes.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2665-9174
Relation: http://www.sciencedirect.com/science/article/pii/S2665917423001939; https://doaj.org/toc/2665-9174
DOI: 10.1016/j.measen.2023.100857
Access URL: https://doaj.org/article/56921cbee0784a3c8a731564fccc0c2c
Accession Number: edsdoj.56921cbee0784a3c8a731564fccc0c2c
Database: Directory of Open Access Journals
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  Data: In order to solve the complexity problem caused by the uncertainty of control system models, this paper utilizes visual sensors and intelligent control technology, and uses data-driven machine learning algorithms to extract representations from the original video for video representation learning, providing crucial semantic features for related tasks. Convolutional Neural Network (CNN) greatly improves the utilization efficiency of visual data and model performance, and realizes the recognition of complex application scenarios. In this paper, an intelligent system control method of time-domain vision sensor is proposed. The proposed method locate, track and measure the speed of moving objects based on CNN and image acquisition device, Asynchronous Temporal Vision Sensor (ATVS). The experimental results show that our proposed algorithm has improved its overall performance through video feature learning and clustering. It not only pays more attention to video spatial information to enhance the discrimination ability of learned video representations, such as scenes and objects, but also improves the tracking performance of visual sensors under various interference attributes.
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      – SubjectFull: Visual sensors
        Type: general
      – SubjectFull: Intelligent systems
        Type: general
      – SubjectFull: Convolutional neural network
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      – SubjectFull: Video representation learning
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      – SubjectFull: Electric apparatus and materials. Electric circuits. Electric networks
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