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Bio-Inspired Object Detection and Tracking in Aerial Images : Harnessing Northern Goshawk Optimization

Pandey, Agnivesh ; Raja, Rohit ; Srivastava, Sumit ; Kumar, Krishna LU ; Gupta, Manoj ; Somthawinpongsai, Chanyanan and Nanthaamornphong, Aziz (2024) In IEEE Access 12. p.174028-174040
Abstract

This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. Object detection and tracking are crucial tasks in various industries, prompting increased exploration of machine learning, particularly deep learning techniques. The proposed NGPGAN model integrates object detection and tracking stages, leveraging the Kalman filter with Northern Goshawk Optimization (NGO) for tracking and employing NGPGAN for detection. To... (More)

This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. Object detection and tracking are crucial tasks in various industries, prompting increased exploration of machine learning, particularly deep learning techniques. The proposed NGPGAN model integrates object detection and tracking stages, leveraging the Kalman filter with Northern Goshawk Optimization (NGO) for tracking and employing NGPGAN for detection. To enhance training stability, Northern Goshawk Optimization is utilized to optimize the generator's cost and loss functions, mitigating issues like non-convergence and mode collapse. The study evaluates the proposed architecture's performance using aerial drone data, focusing on efficiency and accuracy compared to existing methods.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
classifier, Kalman filter, moving objects, non-moving objects, Object detection and tracking
in
IEEE Access
volume
12
pages
13 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85210271086
ISSN
2169-3536
DOI
10.1109/ACCESS.2024.3502033
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2013 IEEE.
id
a43f372d-42a3-45eb-9ca3-f2eaf3542090
date added to LUP
2024-12-15 19:06:42
date last changed
2025-04-04 15:17:56
@article{a43f372d-42a3-45eb-9ca3-f2eaf3542090,
  abstract     = {{<p>This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. Object detection and tracking are crucial tasks in various industries, prompting increased exploration of machine learning, particularly deep learning techniques. The proposed NGPGAN model integrates object detection and tracking stages, leveraging the Kalman filter with Northern Goshawk Optimization (NGO) for tracking and employing NGPGAN for detection. To enhance training stability, Northern Goshawk Optimization is utilized to optimize the generator's cost and loss functions, mitigating issues like non-convergence and mode collapse. The study evaluates the proposed architecture's performance using aerial drone data, focusing on efficiency and accuracy compared to existing methods.</p>}},
  author       = {{Pandey, Agnivesh and Raja, Rohit and Srivastava, Sumit and Kumar, Krishna and Gupta, Manoj and Somthawinpongsai, Chanyanan and Nanthaamornphong, Aziz}},
  issn         = {{2169-3536}},
  keywords     = {{classifier; Kalman filter; moving objects; non-moving objects; Object detection and tracking}},
  language     = {{eng}},
  pages        = {{174028--174040}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Access}},
  title        = {{Bio-Inspired Object Detection and Tracking in Aerial Images : Harnessing Northern Goshawk Optimization}},
  url          = {{http://dx.doi.org/10.1109/ACCESS.2024.3502033}},
  doi          = {{10.1109/ACCESS.2024.3502033}},
  volume       = {{12}},
  year         = {{2024}},
}