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A Low-Level Active ...
A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
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- Danelljan, Martin (author)
- Linköpings universitet,Datorseende,Tekniska högskolan
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- Khan, Fahad Shahbaz (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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- Felsberg, Michael (author)
- Linköpings universitet,Datorseende,Tekniska högskolan
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- Granström, Karl (author)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
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- Heintz, Fredrik (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan
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- Rudol, Piotr (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan
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- Wzorek, Mariusz (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan
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- Kvarnström, Jonas (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan
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- Doherty, Patrick (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska högskolan
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(creator_code:org_t)
- 2015-03-19
- 2015
- English.
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In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I. - Cham : Springer Publishing Company. - 9783319161778 - 9783319161785 ; , s. 223-237
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Abstract
Subject headings
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- Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Visual tracking; Visual surveillance; Micro UAV; Active vision
Publication and Content Type
- ref (subject category)
- kon (subject category)
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