Missing Person Detection System in IoT

Jadhav, Digambar; Chobe, Santoshkumar; Vaibhav, M Venkata Tej; Khandare, Laxman

doi:10.1109/iccubea.2017.8463857

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…This framework proposes to provide live location information for all areas in a smart city/area. The framework recognizes missing persons and sends live photographs and locations of missing persons to nearby police headquarters when they are caught (Jadhav et al, 2017). Using UAVs, Inata et al (Inata et al, 2016) propose a system for detecting missing people that is capable of moving quickly and having a wide field of view during disasters.…”

Section: Related Workmentioning

confidence: 99%

Suhail: A Deep Learning-Based System for Identifying Missing People

Aljohani¹,

Alshehri²,

Alghamdi³

et al. 2023

CIS

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Many people become missing in Saudi Arabia every day, including children, young people, and the mentally ill as well as the elderly with Alzheimer’s. There are many missing people cases that are still unsolved. In Saudi Arabia, people use social media platforms such as Twitter to report missing people cases. The application of deep learning has been successful in a wide range of fields including computer vision and machine vision. In particular, face recognition techniques are effective in saving time and effort, especially when searching for missing individuals. Hence, the goal of this research is to solve this issue by developing a deep learning-based system for identifying missing individuals. This paper introduces a new system called Suhail. The system has been implemented and developed using Android Studio and open-source libraries such as TensorFlow. First, users or governments can report missing persons by uploading photos. Updates and information will then be shared with the rest of the system’s users (volunteers). Once a volunteer discovers a suspect, they scan their face using camera. Then, our application uses face recognition techniques to compare the suspect's photo with photos from the repository. Finally, once a comparison is found, our application contacts the suspect’s family, informs them of his location and then notifies the police that a missing person has been located. By using our application and face recognition systems, we help families and police locate and reach a missing person which saves time and effort. In this study, 759 participants were enrolled to evaluate the performance of the Suhail system. Engagement, aesthetics, and functionality are used to evaluate the user experience. The results of the experiment show that users enjoy the new features of the application and that the system is simple to use. Moreover, the system would help governments and individuals identify missing people faster.

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Section: Related Workmentioning

confidence: 99%

Suhail: A Deep Learning-Based System for Identifying Missing People

Aljohani¹,

Alshehri²,

Alghamdi³

et al. 2023

CIS

View full text Add to dashboard Cite

show abstract

“…• To extricate the fundamental data from the gigantic measure of crude information gathered by the sensor gadgets to yield an important after effect of the considerable amount of information, this activity is performed in this layer [6]. • This layer also helps in doing that by abstracting information, removing unwanted data, and dealing with the information stream.…”

Section: Management Service Layermentioning

confidence: 99%

IoT and Sensor Network

Udgata

Suryadevara

2020

Internet of Things and Sensor Network for COVID-19

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Indexing, enriching, and understanding Brazilian missing person cases from data of distributed repositories on the web

et al. 2022

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For decision making in government, it is necessary to have well-structured sources of information. In several countries, it is difficult to access government data as the information are dispersed, disconnected, and poorly structured. For this reason, this work presents a framework to gather, unify, and enrich missing person data from distributed web sources. The framework allows inserting new tasks specific to the user’s domain to improve data quality. In this study, Brazilian missing person data from non-governmental organizations (NGOs) and governmental websites were collected and semantically enriched. To enhance the understanding of the gathered missing people cases, we create interpretive models using machine learning techniques to extract knowledge and to encourage the use of standards for publishing the data that are frequently ignored by organizations, hindering analysis and decision-making on data. After the collection and semantic enrichment process, there was an increase of approximately 11% in the data present in the base. Also, the mining process evidenced the disappearance and reappearance of a person in Brazil according to several factors such as age, state initiatives, skin tone, hair colors, etc.

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Missing Person Detection System in IoT

Cited by 4 publications

References 8 publications

Suhail: A Deep Learning-Based System for Identifying Missing People

Suhail: A Deep Learning-Based System for Identifying Missing People

IoT and Sensor Network

Indexing, enriching, and understanding Brazilian missing person cases from data of distributed repositories on the web

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