近年來物聯網的應用讓設備之間的聯繫更為緊密，有助於即時傳遞重要 訊息。然而，在現有監控系統中，物聯網的優勢未能充分發揮，許多暴力事 件仍無法及時察覺和干預，導致無法挽回的傷害。這顯示出目前監控系統在 即時檢測和預防暴力事件方面仍有不足。 本論文提出了一種利用樹莓派作為邊緣裝置的智慧監控系統，旨在應對 暴力事件的日益增長及現有監控系統的不足。此系統運用了機器學習技術， 結合移動神經網路（MobileNet）和長短時記憶網路（LSTM），能夠即時檢 測並識別暴力行為，同時向相關單位發送警報，以提高對暴力事件的預防與 即時回應能力。此外，為了兼顧隱私和資料安全，系統採用邊緣運算核心， 這不僅保護了個人隱私，還能高效利用影像資料進行分析。這種設計在增強 公共安全監控效能的同時，也能有效保障個人隱私。 ;In recent years, the application of the Internet of Things （IoT） has significantly tightened the connectivity between devices, facilitating the instantaneous transmission of crucial information. However, the potential of IoT has not been fully realized in existing surveillance systems, and many violent incidents still go undetected and unaddressed in time, resulting in irreparable harm. This highlights the current shortcomings of surveillance systems in the real-time detection and prevention of violent events. This thesis utilizes the Raspberry Pi as an edge device to address the growing prevalence of violent incidents and the inadequacies of existing monitoring systems. By employing advanced machine learning technologies, we develop an intelligent surveillance system. The system integrates Mobile Neural Networks （MobileNet） and Long Short-Term Memory networks（LSTM） to detect and identify violent behaviors in real time. Upon identification, it sends alerts to relevant authorities, enhancing the prevention and immediate response to violent events. Additionally, to balance privacy and data security, the system employs edge computing at its core, safeguarding personal privacy while efficiently analyzing video data. This design not only enhances the effectiveness of public safety monitoring but also effectively protects individual privacy.
