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Opt-in Camera: Person Identification in Video via UWB Localization and Its Application to Opt-in Systems
Robotics · Submitted 30 Sep 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | contribution 0.98 | 0.49 | 0.20 | asserted 0.98 |
| s1 | 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. | method 0.81 | 0.77 | 0.05 | asserted 1.00 |
| s2 | Specifically, the on-ground positions of the wireless tag are first tracked over time using the unscented Kalman filter (UKF). | method 1.00 | 0.85 | 0.02 | asserted 0.99 |
| s3 | The tag trajectory is then matched against visual tracking results for pedestrians found in videos to identify the tag carrier. | method 1.00 | 0.85 | 0.05 | asserted 1.00 |
| s4 | 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. | method 0.57 | 0.95 | 0.05 | asserted 1.00 |
| s5 | We implemented the proposed opt-in camera system using ultra-wideband (UWB) devices and an off-the-shelf webcam. | method 0.96 | 0.96 | 0.03 | asserted 1.00 |
| s6 | 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. | result 1.00 | 0.97 | 0.65 | asserted 1.00 |
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The paper
Original 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.
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Uses the method or tool of 2 papers
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=-1 We apply object detection [ref] and multi-object tracking (MOT) [ref] algorithms to video footage to obtain pedestrian trajectories in the image space, as shown by the solid cyan, green, and red curves in Fig. [ref].
YOLOX: Exceeding YOLO Series in 2021 · Camera-based Tracking · 1.00
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Second, we switch the UKF's observation noise covariance matrix $R$ between NLoS ($R=R_NLoS$) and LoS ($R=R_LoS$) based on the detector $g(·)$: [equation] We determine $R_NLoS$ and $R_LoS$ (both diagonal matrices) via black-box optimization with CMA-ES [ref] by minimizing: [equation] where $D(·, ·)$ denotes Mahalanobis distance.
2402.01373 · NLoS-aware Parameter Tuning for Kalman Filter · 1.00
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