# Exploring Public's Perception of Safety and Video Surveillance Technology: A Survey Approach

arXiv: https://arxiv.org/abs/2312.06707 · HTML: https://mlsanity.com/paper/2312.06707/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark](https://mlsanity.com/paper/2408.14329/) (2024)
- [A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment](https://mlsanity.com/paper/2508.14203/) (2025)
- [ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds](https://mlsanity.com/paper/2508.09058/) (2025)
- [From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection](https://mlsanity.com/paper/2604.09327/) (2026)

## 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.