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 realize the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam installed in the environment. Experimental results demonstrate that our system can perform opt-in recording of individuals in near real-time at 10 fps, with reliable identification accuracy for a crowd of 8-23 people in a confined space.

翻译：本文提出"选择性摄像"这一概念，即一种隐私保护型摄像系统，能够仅记录人群中明确同意被拍摄的特定个体。本系统利用附着于个人物品的移动无线通信标签作为参与选择的凭证，并作为视频中定位标签携带者的手段。具体而言，系统首先采用无迹卡尔曼滤波器（UKF）对无线标签的地面位置进行时序追踪，随后将标签轨迹与视频中检测到的行人视觉追踪结果进行匹配，从而识别标签携带者。在技术层面，我们设计了基于约束线性优化的专用轨迹匹配方法，并提出一种新型标定技术，该技术可同时处理无线标签-摄像机的标定问题以及UKF的超参数调优，从而缓解无线定位中的非视距（NLoS）问题。我们采用超宽带（UWB）设备与环境部署的商用网络摄像头实现了所提出的选择性摄像系统。实验结果表明，本系统能以10帧/秒的速度近实时完成个体选择性记录，在受限空间内对8-23人的人群实现可靠的识别精度。