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An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm
This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. Utilizing a Raspberry Pi 3 as the core processing unit, the system employs a motion-detecting camera to monitor a defined area, recording and securely archiving video data on a cloud server upon detecting movement. This cloud-based repository supports real-time analysis, ensuring that data remains available for future reference. Battery-powered configuration enhances the system’s portability, making it adaptable across various environments, such as healthcare for patient monitoring or wildlife tracking for behavioural studies. The design aligns with IoT principles, featuring autonomous operation and cloud connectivity, offering a scalable, flexible solution capable of integration into larger IoT ecosystems for diverse surveillance applications.
An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm
This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. Utilizing a Raspberry Pi 3 as the core processing unit, the system employs a motion-detecting camera to monitor a defined area, recording and securely archiving video data on a cloud server upon detecting movement. This cloud-based repository supports real-time analysis, ensuring that data remains available for future reference. Battery-powered configuration enhances the system’s portability, making it adaptable across various environments, such as healthcare for patient monitoring or wildlife tracking for behavioural studies. The design aligns with IoT principles, featuring autonomous operation and cloud connectivity, offering a scalable, flexible solution capable of integration into larger IoT ecosystems for diverse surveillance applications.
An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm
Zaid Sh. Bakr (Autor:in) / Hamzah M. Marhoon (Autor:in) / Ammar Alaythawy (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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