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Dronecaps : recognition of human actions in drone videos using capsule networks with binary volume comparisons

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Algamdi, Abdullah M., Sanchez Silva, Victor and Li, Chang-Tsun (2020) Dronecaps : recognition of human actions in drone videos using capsule networks with binary volume comparisons. In: 27th IEEE International Conference on Image Processing, Abu Dhabi, 25-28 Oct 2020. Published in: 2020 IEEE International Conference on Image Processing (ICIP) ISSN 1522-4880. doi:10.1109/ICIP40778.2020.9190864

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Official URL: https://doi.org/10.1109/ICIP40778.2020.9190864

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Abstract

Understanding human actions from videos captured by drones is a challenging task in computer vision due to the unfamiliar viewpoints of individuals and changes in their size due to the camera’s location and motion. This work proposes DroneCaps, a capsule network architecture for multi-label human action recognition (HAR) in videos captured by drones. DroneCaps uses features computed by 3D convolution neural networks plus a new set of features computed by a novel Binary Volume Comparison layer. All these features, in conjunction with the learning power of CapsNets, allow understanding and abstracting the different viewpoints and poses of the depicted individuals very efficiently, thus improving multi-label HAR. The evaluation of the DroneCaps architecture’s performance for multi-label classification shows that it outperforms state-of-the-art methods on the Okutama-Action dataset.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Drone aircraft , Drone aircraft in remote sensing, Computer vision , Pattern recognition system, Human activity recognition , Computer network architectures , Three-dimensional imaging , Neural networks (Computer science) , Adaptive routing (Computer network management)
Journal or Publication Title: 2020 IEEE International Conference on Image Processing (ICIP)
Publisher: IEEE
ISSN: 1522-4880
Official Date: 30 September 2020
Dates:
DateEvent
30 September 2020Published
16 May 2020Accepted
2 February 2020Submitted
DOI: 10.1109/ICIP40778.2020.9190864
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: IEEE
Conference Paper Type: Paper
Title of Event: 27th IEEE International Conference on Image Processing
Type of Event: Conference
Location of Event: Abu Dhabi
Date(s) of Event: 25-28 Oct 2020
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