Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety

Ardabili, Babak Rahimi; Pazho, Armin Danesh; Noghre, Ghazal Alinezhad; Neff, Christopher; Bhaskararayuni, Sai Datta; Ravindran, Arun; Reid, S.; Tabkhi, Hamed

doi:10.48550/arxiv.2302.04310

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“…In this study, we utilized a designed scalable SVS system [10] to conduct the Artificial Intelligence (AI) vision analysis and detect anomalous behaviors. We also employed a smartphone application [11] to push notifications to the end user in case of detecting anomalies. Figure 1 represents the conceptual end-to-end model used in this study.…”

Section: Introductionmentioning

confidence: 99%

Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College

Yao¹,

Ardabili²,

Pazho³

et al. 2023

Preprint

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Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging.This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camerabased system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for realtime notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities' privacy and ethical requirements as well as hardware configuration and setups.We evaluate the system's performance using throughput and end-to-end latency. The experiment results show that, on average, our system's end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate.

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Section: Introductionmentioning

confidence: 99%