Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approach
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Abstract (arXiv)
Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the community's general public safety concerns, their view of existing surveillance technologies, and their perception of AI-driven solutions for enhancing safety in urban environments, focusing on Charlotte, NC. Through a survey approach, including in-person surveys conducted in August and September 2023 with 410 participants, this research investigates demographic factors such as age, gender, ethnicity, and educational level to gain insights into public perception and concerns toward public safety and possible solutions. Based on the type of dependent variables, we utilized different statistical and significance analyses, such as logit regression and ordinal logistic regression, to explore the effects of demographic factors on the various dependent variables. Our results reveal demographic differences in public safety concerns. Younger females tend to feel less secure yet trust existing video surveillance systems, whereas older, educated individuals are more concerned about violent crimes in malls. Additionally, attitudes towards AI-driven surveillance differ: older Black individuals demonstrate support for it despite having concerns about data privacy, while educated females show a tendency towards skepticism.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmarkcited by 6
- 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
- 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
- 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detectioncited by 1
- 2026From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection10 Apr 2026
- 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment19 Aug 2025
- 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds12 Aug 2025
- 2024Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark26 Aug 2024