mlsanityData to 7 Oct 2026

Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety

arXiv 2212.12936 ↗First posted 25 Dec 2022cs.CY cs.AI cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewing four policy protection regulations that generate an overview of best practices for privacy protection, we argue that ethical and privacy concerns could be addressed through four lenses: algorithm, system, model, and data. As an case study, we describe our proposed system and illustrate how our system can create a baseline for designing a privacy perseverance system to deliver safety to society. We used several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system. We also use cloud-native services to implement a smartphone application in order to deliver the outputs to the end users.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 25 Dec 2022.

Builds on

15 of 40 references.

References · 15

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  4. 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 812
  5. 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 547
  6. 2018Simple Baselines for Human Pose Estimation and Trackingcited by 502
  7. 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 497
  8. 2020MOT20: A benchmark for multi object tracking in crowded scenescited by 355
  9. 2015MOTChallenge 2015: Towards a Benchmark for Multi-Target Trackingcited by 133
  10. 2019Peeking into the Future: Predicting Future Person Activities and Locations in Videoscited by 112
  11. 2019Graph Embedded Pose Clustering for Anomaly Detectioncited by 95
  12. 2018Iterative Crowd Countingcited by 48
3 more
  1. 2019CVPR19 Tracking and Detection Challenge: How crowded can it get?cited by 22
  2. 2019REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Trackingcited by 14
  3. 2022Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillancecited by 6

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 3.

7 papers.