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

TL;DR

This paper presents a comprehensive AI-enabled smart video surveillance system that emphasizes privacy preservation, combining computer vision, data analytics, and cloud services, while evaluating its performance and privacy trade-offs.

Contribution

It introduces the first end-to-end privacy-preserving smart video surveillance system integrating multiple analytics and proposes metrics for system evaluation.

Findings

Achieves 17.8 FPS in extreme scenes

Privacy considerations lead to using pose-based algorithms

System drops in accuracy but enhances privacy protection

Abstract

Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48 to 73.72 in anomaly detection and 96 to 83.07 in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.