RETRACTED: Motion image detection algorithm for juvenile obesity based on video surveillance and internet of things

Tang, Nana

doi:10.1016/j.micpro.2020.103788

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Video surveillance systems used in public locations must deal with large object traffic density, as well as heterogeneous, variable and occlusions of moving objects. Such systems must be able to detect complex and sudden events [5], and observe the activities, behavior and other immediate information regarding objects. Conventional video surveillance needs a lot of storage space, since it records and captures everything observed by a surveillance camera, and this requires much of the storage resources of the system devices.…”

Section: Introductionmentioning

confidence: 99%

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Hamad¹

2021

IJCDS

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Intelligent video surveillance systems based on Internet of Things (IoT) technology have proven to be major primary components for security in many areas, such as smart cities. These systems are important because they provide messages that transfer information about people on campus among camera nodes, thereby providing real-time video surveillance monitoring. The proposed system consists of several cameras and an intelligent processing system, represented by a raspberry pi. The cameras are distributed in different locations in the university campus. Each camera node is connected to the internet and can communicate and share information with other nodes, as well as communicate with a central monitoring (server) via Message Queuing Telemetry Transport (MQTT) IoT protocol. The cameras can extract information in real time from video, and identify everyone as either students, teachers and/or employees using computer vision algorithms. Two methods of face detection and recognition techniques are applied: a feature-based technique that uses the Haar cascade, and an image-based technique that uses k-nearest neighbour (kNN). Face detection and recognition based on the Haar cascade classifier is more suitable for resources with embedded limited systems since it requires less computation, while kNN is more accurate and shows better results in a dynamic environment. All programs were written using opensource Python under a Linux operating system and by using OpenCV library.

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Section: Introductionmentioning

confidence: 99%

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Hamad¹

2021

IJCDS

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Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner

et al. 2022

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The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.

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RETRACTED: Motion image detection algorithm for juvenile obesity based on video surveillance and internet of things

Cited by 2 publications

References 14 publications

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner

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