The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In low-brightness environment, DaCoLT detects knifes with an accuracy 87.74%.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
In the citing paper
“This study proposed a new pre-processing technique called darkening and contrast at learning and test (DaCoLT) in 2019 that utilizes brightness guidance to overcome the negative effects of varying illumination.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
In the citing paper
“After it applied DaCoLT, a CNN-based detection model was trained and evaluated on low-quality videos, and it demonstrated high potential as an automatic alarm system by its satisfactory results.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
In the citing paper
“Additionally, the frame processing time of DaCoLT might experience delays due to sudden knife movements.”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [128]
Citations
Cites
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Flagged first, then by citations
Research works printed but not linked: 1
- Huang, 2017, Tensorflow object detection api, CVPR
Cited by
Sorted by citations
Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., & Herrera, F. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105590 · Cited by 196
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
“The probability of false positives that occur due to problems in illumination was discussed in [128].”
BackgroundA Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets
Shidik, G. F., Noersasongko, E., Nugraha, A., Andono, P. N., Jumanto, J., & Kusuma, E. J. (2019) · IEEE Access · doi:10.1109/access.2019.2955387 · Cited by 114
Metadata
- Authors
- Castillo, A., Tabik, S., Pérez, F., Olmos, R., & Herrera, F.
- Affiliations
- Universidad de Granada (ES)
- Corresponding author
- Castillo, Alberto
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 330
- Pages
- 151-161
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2900484583
- Cited by
- 122
- References
- 31
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 22 · 2023: 20 · 2022: 23 · 2021: 19 · 2020: 10 · 2019: 4
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neurocomputing (publishedVersion)
- Funders
- Spanish Ministry of Science and Technology (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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- video surveillance, metallic object detection, illumination robustness, surface reflectance, automated alert system, convolutional neural networks
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