EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching

Fang, Shiwei; Islam, Tamzeed; Munir, Sirajum; Nirjon, Shahriar

doi:10.1109/dcoss49796.2020.00022

Cited by 17 publications

(6 citation statements)

References 24 publications

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“…The work [36] applies the same approach to re-identify the body-worn IMUs from the video. The studies [3,8,27] associate camera data with Wi-Fi data for various purposes of augmenting the camera with depth information [3] or simultaneous human subject identification and tracking [8]. The work [20] associates users' smartphone Wi-Fi fine timing measurements and IMU data with a camera footage.…”

Section: Multimentioning

confidence: 99%

“…ImmTrack belongs to the cross-modality data association category. Different from the existing studies [3,8,20,27,36] that use camera as an association source, we employ mmWave radar that is less privacy-intrusive. Moreover, technically, mmWave radar directly provides 3D locations and velocities of the human subjects, which facilitate the association.…”

Section: Multimentioning

confidence: 99%

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Interpersonal Distance Tracking with mmWave Radar and IMUs

Dai¹,

Shuai²,

Tan³

et al. 2023

Preprint

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Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacyintrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. CCS CONCEPTS• Human-centered computing → Ambient intelligence; Mobile computing; • Computer systems organization → Sensor networks.

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Section: Multimentioning

confidence: 99%

Section: Multimentioning

confidence: 99%

Interpersonal Distance Tracking with mmWave Radar and IMUs

Dai¹,

Shuai²,

Tan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The work [35] applies the same approach to re-identify the body-worn IMUs from the video. The studies [3,8,27] associate camera data with Wi-Fi data for various purposes of augmenting the camera with depth information [3] or simultaneous human subject identification and tracking [8]. The work [20] associates users' smartphone Wi-Fi fine timing measurements and IMU data with a camera footage.…”

Section: Multimentioning

confidence: 99%

The 22nd International Conference on Information Processing in Sensor Networks

2023

View full text Add to dashboard Cite

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. CCS CONCEPTS• Human-centered computing → Ambient intelligence; Mobile computing; • Computer systems organization → Sensor networks.

show abstract

“…The work [53] applies the same approach to re-identify the body-worn IMUs from the video. The works [54][55][56] associate camera data with Wi-Fi data for various purposes of augmenting the camera with depth information [54] or simultaneous human subject identification and tracking [55]. The work [57] associates users' smartphone Wi-Fi fine timing measurements and IMU data with a camera footage.…”

Section: Multi-modality Data Processingmentioning

confidence: 99%

“…ImmTrack belongs to the cross-modality data association category. Different from the existing works [52][53][54][55][56][57] that use camera as an association source, ImmTrack employs mmWave radar that is less privacy-intrusive. Moreover, technically, mmWave radar directly provides 3D locations and velocities of the human subjects, which facilitate the association.…”

Section: Multi-modality Data Processingmentioning

confidence: 99%

Interpersonal distance tracking with mmWave radar and IMUs

Dai¹

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The contributions of the co-authors are as follows:• Prof. Rui Tan provided the initial research direction.• Prof. Guoliang Xing made critical comments on the research.• Prof. Rui Tan and Dr. Xian Shuai assisted in the proof of concept process.• Dr. Xian Shuai assisted in data collection pipeline and the system architecture design.• Yimin Dai co-designed the point cloud deep learning model with Dr. Xian Shuai.

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EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching

Cited by 17 publications

References 24 publications

Interpersonal Distance Tracking with mmWave Radar and IMUs

Interpersonal Distance Tracking with mmWave Radar and IMUs

The 22nd International Conference on Information Processing in Sensor Networks

Interpersonal distance tracking with mmWave radar and IMUs

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