Person Tracking by Fusing Posture Data from UAV Video and Wearable Sensors

Alisher Mukashev; Lan Da Van; Susanta Sharma; M. Farhan Tandia; Yu Chee Tseng

doi:10.1109/JSEN.2022.3218373

Person Tracking by Fusing Posture Data from UAV Video and Wearable Sensors

Alisher Mukashev, Lan Da Van, Susanta Sharma, M. Farhan Tandia, Yu Chee Tseng

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, a novel framework that fuses the posture data taken by a drone (or unmanned aerial vehicle, UAV) camera and the wearable sensors data recorded by smartwatches is proposed. The framework is designed for continuously tracking persons in a drone view by analyzing location-independent human posture features and correctly tagging smartwatch identities (IDs) and personal profiles to video human objects, thus conquering the former work in requiring ground markers. Person detection, ID assignment, and pose estimation are integrated into our framework to obtain recognized human postures. These recognized postures are then paired with those from the wearable sensors. Through fusing common postures such as standing, walking, jumping, and falling down, person tracking accuracy by UAV up to 95.36% can be attained in our testing scenarios.

Original language	English
Pages (from-to)	1
Number of pages	1
Journal	IEEE Sensors Journal
DOIs	https://doi.org/10.1109/JSEN.2022.3218373
State	Accepted/In press - 2022

Keywords

Action recognition
Autonomous aerial vehicles
Cameras
data fusion
drone
Drones
person identification
person tracking
Sensors
Skeleton
Target tracking
wearable devices
Wearable sensors

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10.1109/JSEN.2022.3218373

Cite this

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title = "Person Tracking by Fusing Posture Data from UAV Video and Wearable Sensors",

abstract = "In this paper, a novel framework that fuses the posture data taken by a drone (or unmanned aerial vehicle, UAV) camera and the wearable sensors data recorded by smartwatches is proposed. The framework is designed for continuously tracking persons in a drone view by analyzing location-independent human posture features and correctly tagging smartwatch identities (IDs) and personal profiles to video human objects, thus conquering the former work in requiring ground markers. Person detection, ID assignment, and pose estimation are integrated into our framework to obtain recognized human postures. These recognized postures are then paired with those from the wearable sensors. Through fusing common postures such as standing, walking, jumping, and falling down, person tracking accuracy by UAV up to 95.36% can be attained in our testing scenarios.",

keywords = "Action recognition, Autonomous aerial vehicles, Cameras, data fusion, drone, Drones, person identification, person tracking, Sensors, Skeleton, Target tracking, wearable devices, Wearable sensors",

author = "Alisher Mukashev and Van, {Lan Da} and Susanta Sharma and {Farhan Tandia}, M. and Tseng, {Yu Chee}",

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AU - Tseng, Yu Chee

N1 - Publisher Copyright: IEEE

PY - 2022

Y1 - 2022

N2 - In this paper, a novel framework that fuses the posture data taken by a drone (or unmanned aerial vehicle, UAV) camera and the wearable sensors data recorded by smartwatches is proposed. The framework is designed for continuously tracking persons in a drone view by analyzing location-independent human posture features and correctly tagging smartwatch identities (IDs) and personal profiles to video human objects, thus conquering the former work in requiring ground markers. Person detection, ID assignment, and pose estimation are integrated into our framework to obtain recognized human postures. These recognized postures are then paired with those from the wearable sensors. Through fusing common postures such as standing, walking, jumping, and falling down, person tracking accuracy by UAV up to 95.36% can be attained in our testing scenarios.

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KW - Cameras

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KW - drone

KW - Drones

KW - person identification

KW - person tracking

KW - Sensors

KW - Skeleton

KW - Target tracking

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Person Tracking by Fusing Posture Data from UAV Video and Wearable Sensors

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Keywords

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