A binocular image fusion approach for minimizing false positives in handgun detection with deep learning
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In [127], with a dataset of 124 images, accuracy rate was 80.62%.
“while with a dataset of 124 images, the accuracy rate stood only at 80.62%”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [127]
In [127], as the number of images in the dataset increases, accuracy rate rises.
“In implementation, as the number of images within the dataset increases, there is a corresponding rise in the accuracy rate.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [127]
The composite image from [127] provides three-dimensional information about objects, which can help reduce false positives during classification.
“This composite image provides three-dimensional information about objects within the scene, which can help to reduce the occurrence of false positives during classification.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [127]
In [127], expanding dataset to 332 images improved accuracy to 87.93%.
“the accuracy rate notably improved, reaching 87.93% upon expanding the dataset to 332 images.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [127]
The approach in [127] fuses binocular images from multiple viewpoints to create a comprehensive representation of a scene.
“The approach presented in [127] involves fusing binocular images captured from multiple viewpoints to create a comprehensive and informative representation of a scene.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [127]
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Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119 · see citing works
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Self-citationA Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“The approach presented in [127] involves fusing binocular images captured from multiple viewpoints to create a comprehensive and informative representation of a scene.”
Background
- Authors
- Olmos, R., Tabik, S., Lamas, A., Pérez-Hernández, F., & Herrera, F.
- Affiliations
- Universidad de Granada (ES)
- Corresponding author
- Tabik, Siham
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 49
- Pages
- 271-280
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2902261126
- Cited by
- 57
- References
- 26
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 12 · 2023: 11 · 2022: 4 · 2021: 12 · 2020: 3 · 2019: 4
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Fusion (publishedVersion)
- Funders
- Ministerio de Ciencia y Tecnologia (TIN2017-89517-P)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- handgun detection, false positive reduction, dual-camera system, surveillance video, object detection, candidate region selection
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