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Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting

Sensors (Basel, Switzerland), 2022-09, Vol.22 (18), p.7020 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22187020 ;PMID: 36146369

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  • Title:
    Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting
  • Author: Ahmad, Tasweer ; Cavazza, Marc ; Matsuo, Yutaka ; Prendinger, Helmut
  • Subjects: Ablation ; action detection ; Adaptation ; Cameras ; Classifiers ; Datasets ; Deep learning ; Drone aircraft ; Drone vehicles ; Drones ; Environmental monitoring ; Evacuations & rescues ; Flight altitude ; gradient boosting classifier ; Human activity recognition ; Human motion ; Image acquisition ; Localization ; Machine learning ; Methods ; Neural networks ; Occlusion ; Performance evaluation ; Sensors ; Unmanned aerial vehicles ; YoloV5
  • Is Part Of: Sensors (Basel, Switzerland), 2022-09, Vol.22 (18), p.7020
  • Description: Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s22187020
    PMID: 36146369
  • Source: GFMER Free Medical Journals
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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