Abstract
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 camera-based 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 real-time 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.
Abstract (translated)
智能视频监控系统最近对于确保公共安全和安保至关重要，尤其是在智慧城市中。然而，应用实时人工智能技术并与低延迟通知和警报相结合，使部署这些系统变得非常具有挑战性。本文介绍了基于一所社区大学的现实世界测试平台的设计和应用智能视频监控系统的案例分析。我们主要关注一个基于智能摄像机的系统，能够立即识别异常活动并通知 stakeholders和居民。本文重点强调并解决了不同算法和系统设计挑战，以确保测试平台上的实时高准确性视频分析处理。此外，本文还展示了云系统基础设施和实时通知的移动应用程序的例子，以保持学生、教师/工作人员和负责任的安全人员参与循环。同时，本文还涵盖了设计决定，以维护社区隐私和伦理要求，以及硬件配置和setup。我们使用吞吐量和端到端延迟来评估系统的性能。实验结果显示，在运行1、4和8个摄像机的情况下，如果检测到异常活动，系统的平均端到端延迟通知用户为5.3、5.78和11.11秒。另一方面，如果检测到异常行为，系统可以通知用户，平均延迟为7.3、7.63和20.78秒。这些结果表明，系统在合理的时间内有效地检测到并通知异常行为和可疑物品。系统可以同时运行8个摄像机，以每秒32.41帧的速度运行。
URL
https://arxiv.org/abs/2303.12934
