Things in the air: tagging wearable IoT information on drone videos

Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to 3D space. An interesting question is: Can we conduct person identification (PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/ir...

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Bibliographic Details
Published in:Discover Internet of things 2021-12, Vol.1 (1), Article 6
Main Authors: Van, Lan-Da, Zhang, Ling-Yan, Chang, Chun-Hao, Tong, Kit-Lun, Wu, Kun-Ru, Tseng, Yu-Chee
Format: Article
Language:English
Subjects:
Accuracy
Biometric identification
Biometrics
Cameras
Computer Science
Cyber-physical systems
Deep learning
Drones
Electrical Engineering
Facial recognition technology
Information Systems Applications (incl.Internet)
Internet of Things
IoT
Physiology
Sensors
Surveillance
Unmanned aerial vehicles
Wearable computers
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Summary:Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to 3D space. An interesting question is: Can we conduct person identification (PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/iris/face recognition have their limitations or require close contact to specific devices. Hence, these traditional technologies can not be easily deployed to drones due to dynamic change of view angle and height. In this work, we demonstrate how to retrieve IoT data from users’ wearables and correctly tag them on the human objects captured by a drone camera to identify and track ground human objects. First, we retrieve human objects from videos and conduct coordination transformation to handle the change of drone positions. Second, a fusion algorithm is applied to measure the correlation of video data and inertial data based on the extracted human motion features. Finally, we can couple human objects with their wearable IoT devices, achieving our goal of tagging wearable device data (such as personal profiles) on human objects in a drone view. Our experimental evaluation shows a recognition rate of 99.5% for varying walking paths, and 98.6% when the drone’s camera angle is within 37°. To the best of our knowledge, this is the first work integrating videos from drone cameras and IoT data from inertial sensors.
ISSN:2730-7239
2730-7239
DOI:10.1007/s43926-021-00005-8