Abstract:
Rwanda's Genocide Memorial Centres are powerful symbols of the country's tragic past, carrying substantial historical and cultural weight. Nevertheless, these locations are susceptible to encroachment and vandalism, both of which have the potential to harm the healing process and tarnish the memory of the victims. In response to this difficulty, An AI and IoT based security system has been proposed. Strategically positioned throughout the memorial centre are motion detectors, cameras, light-level detectors (LLDR), and buzzers that comprise the system. In response to motion sensor detection of unauthorised movements, nearby cameras initiate the process of capturing images of potential intruders. Real-time analysis of these images by Convolutional Neural Network (CNN) algorithms detects suspicious activity. By differentiating between day and night conditions, the LLDR sensors guarantee precise threat detection. The system activates its camera to transmit real-time video to a centralised control room in response to detected potential threats. In addition, sophisticated image processing and CNN algorithms scrutinise the footage to detect atypical activities. Additionally, a buzzer is promptly triggered by the system, notifying on-site security personnel. By means of our web interface, authorised personnel are granted access to real-time video feeds and sensor data via remote monitoring functionalities. This significantly mitigates the requirement for a perpetual on-site security presence and facilitates prompt off-site responses. The preservation of the integrity of these memorial sites serves to guard the remembrance of the victims and facilitates the process of national reconciliation. The integration of CNN algorithms, motion sensors, cameras, LLDR sensors, and buzzers serves as a prime illustration of the efficacy of IoT solutions in safeguarding and conserving historic sites of significance. The potential of technology-driven strategies to bolster security measures for historical and cultural assets is highlighted in this case study.
