Deep Learning: Foundations and Concepts
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CNNs support detecting and classifying individuals even with background noise or occlusions.
“They support detecting and classifying individuals even with background noise or occlusions.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [52]
Combined with CNNs or other classifiers, background subtraction can improve accuracy by reducing the false positives of nonpassenger objects.
“Combined with CNNs [52] or other classifiers, background subtraction can improve accuracy by reducing the false positives of nonpassenger objects.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [52]
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- Authors
- Bishop, C. M., & Bishop, H.
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2024
- OpenAlex
- W4388152645
- Cited by
- 220
- References
- 0
- Citations by year
- 2026: 81 · 2025: 92 · 2024: 47
- FWCI
- 38.61
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- deep learning, deep learning architectures, artificial neural networks, machine learning methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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