Scale Invariant Privacy Preserving Video via Wavelet Decomposition

TL;DR

This paper introduces a scale-invariant privacy-preserving video method using wavelet decomposition to effectively anonymize objects at various distances without losing detail.

Contribution

It presents a novel wavelet-based approach that maintains privacy across different scales, addressing limitations of previous methods.

Findings

Effective anonymization of objects at multiple scales

Preserves video detail while ensuring privacy

Outperforms existing scale-dependent methods

Abstract

Video surveillance has become ubiquitous in the modern world. Mobile devices, surveillance cameras, and IoT devices, all can record video that can violate our privacy. One proposed solution for this is privacy-preserving video, which removes identifying information from the video as it is produced. Several algorithms for this have been proposed, but all of them suffer from scale issues: in order to sufficiently anonymize near-camera objects, distant objects become unidentifiable. In this paper, we propose a scale-invariant method, based on wavelet decomposition.