CSIS Ecosystem: Impacts of Pragmatic Detection on a Campus Surveillance Case Study

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Authors: Babacar Mané, Fernando H. de A. Moraes Neto, Roberto de Cerqueira Figueiredo, Caio Nery, Diana Romero Clavijo, Samuel Rios da Silva, Daniela Barreiro Claro, Célia Ghedini Ralha, Ana Patrícia Magalhães, Rita Suzana Pitangueira Maciel, Marlo Souza, George Marconi de Araújo Lima
+5 more Bruno Pereira dos Santos, Robespierre Pita, Edlane Proencia, Luis Emanuel Neves de Jesus, Iala Patrícia de Jesus Monteiro de Jesus
Cited by: 1
References: 23

Abstract

Research Context: In modern universities, where there is a constant flow of people, it is essential to implement computer vision surveillance systems to detect incidents and alert security personnel, in order to ensure the safety of all members of the academic community. Practical Problem: Universities are faced with numerous security issues, including limited resources, a diverse range of technical infrastructure, and a mix of old and new systems. Besides, traditional security methods are reactive, slow to notice incidents, which in turn makes quick response in emergencies very hard. Proposed Solution: We present the solution in the form of the Campus Surveillance Interoperability System (CSIS), which is an open-source, modular, and interoperable architecture that we put forth for the specific surveillance needs of universities. CSIS utilizes a variety of computer vision models from the YOLOv11x set, specialized in recognizing weapons, fires, floods, graffiti, suspicious behavior, and license plates. Related IS Theory: Our paper adopts a Socio-technical theory, which recognizes that the effectiveness of information systems depends on the interaction between social and technical components rather than on technology alone. Research Method: In a real-world university setting, we put our proof of concept to the test. We had real-time video stream input, ensemble-based inference, and a centralized alert management system. We assessed the accuracy of the models. Summary of Results: We achieved an average accuracy of 83% for license plate recognition, weapon detection, graffiti, and smoke detections. Although we achieved moderate accuracy in fire detection and lower performance in flood detection. Considering sociotechnical concerns, we evaluate CSIS’s ability to automate surveillance tasks. Contributions and Impact to IS Area: CSIS architecture introduces an interoperable architecture for intelligent surveillance, demonstrating a model of interoperability in real time;

Fields of Study & Subjects

Fire Detection and Safety Systems IoT and GPS-based Vehicle Safety Systems IoT-based Smart Home Systems

Publication details

Journal / source
Anais do XXII Simpósio Brasileiro de Sistemas de Informação (SBSI 2026)
Publisher
Sociedade Brasileira de Computação
Pages
1219–1238
Publication date
5 May 2026