Abstract
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.
Highlights
Drones have been applied to a lot of fields, such as smart agriculture [2], military [3], construction site monitoring [4], and environmental monitoring [5]
We intend to tackle the challenge of integrating visual data and IoT data for person identification (PID), where human objects captured by a drone camera can be correctly tagged by their wearable IoT information
We present an approach to identifying and tracking human objects in the videos taken from a drone and correctly tagging their personal profiles retrieved, through wireless communications, from their wearable IoT devices
Summary
Drones (or Unmanned Aerial Vehicles, UAVs) have been applied to a lot of fields, such as smart agriculture [2], military [3], construction site monitoring [4], and environmental monitoring [5]. This work investigates what future surveillance system would look like. We observe that both cameras and IoT devices have their particular capabilities in identifying and tracking moving objects and their behaviors. We intend to tackle the challenge of integrating visual data and IoT data for person identification (PID), where human objects captured by a drone camera can be correctly tagged by their wearable IoT information (such as personal profiles)
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