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

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arxiv 2212.12936 v1 pith:OPZXUHDN submitted 2022-12-25 cs.CY cs.AIcs.CV

classification cs.CYcs.AIcs.CV
keywords privacysystemsystemsethicalpublicsafetysurveillancechallenges
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

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  1. TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing

    cs.DB 2025-07 reject novelty 5.0 of 10

    A camera-network query system uses learned long-term correlations and adaptive windowed search to accelerate object re-identification, claiming 3.9x over Spatula with recall left unverified.

  2. Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose

    cs.CV 2025-04 conditional novelty 5.0 of 10

    Shopformer detects shoplifting by training a transformer to reconstruct pose-sequence tokens, scoring anomalies by reconstruction error, and reports 69.15% AUC-ROC on the PoseLift dataset.

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