Opt-in Camera: Person Identification in Video via UWB Localization and Its Application to Opt-in Systems

TL;DR

This paper introduces an opt-in camera system that combines UWB-based wireless localization with visual tracking to identify and record only consenting individuals in crowds, enhancing privacy in video surveillance.

Contribution

The paper presents a novel integration of UWB localization with visual tracking, including new calibration and trajectory matching techniques for privacy-preserving person identification.

Findings

Real-time operation at 10 fps

Reliable identification in crowds of 8-23 people

Effective handling of non-line-of-sight wireless localization

Abstract

This paper presents opt-in camera, a concept of privacy-preserving camera systems capable of recording only specific individuals in a crowd who explicitly consent to be recorded. Our system utilizes a mobile wireless communication tag attached to personal belongings as proof of opt-in and as a means of localizing tag carriers in video footage. Specifically, the on-ground positions of the wireless tag are first tracked over time using the unscented Kalman filter (UKF). The tag trajectory is then matched against visual tracking results for pedestrians found in videos to identify the tag carrier. Technically, we devise a dedicated trajectory matching technique based on constrained linear optimization, as well as a novel calibration technique that handles wireless tag-camera calibration and hyperparameter tuning for the UKF, which mitigates the non-line-of-sight (NLoS) issue in wireless localization. We implemented the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam. Experimental results demonstrate that our system can perform opt-in recording of individuals in real-time at 10 fps, with reliable identification accuracy in crowds of 8-23 people in a confined space.