Scale Invariant Privacy Preserving Video via Wavelet Decomposition
This addresses privacy concerns in video surveillance for users of mobile devices and IoT systems, but appears incremental as it builds on existing privacy-preserving video methods.
The paper tackles the problem of scale issues in privacy-preserving video algorithms, where anonymizing near-camera objects makes distant objects unidentifiable, and proposes a scale-invariant method based on wavelet decomposition.
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.