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Paper

Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey

by Independent / Community 0133c0bcfedec4e9d15c797acf9574bcf6f56d19

Free2AITools Nexus Index

70.0

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 86

P: Popularity 63

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of differe...

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Semantic Scholar 94 Citations

Paper Information Summary
Entity Passport
Registry ID	0133c0bcfedec4e9d15c797acf9574bcf6f56d19
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0133c0bcfedec4e9d15c797acf9574bcf6f56d19,
  author = {Unknown},
  title = {Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0133c0bcfedec4e9d15c797acf9574bcf6f56d19}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey [Paper]. Free2AITools. https://api.semanticscholar.org/0133c0bcfedec4e9d15c797acf9574bcf6f56d19

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 86

Popularity (P) 63

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey: Authority (A:86), Popularity (P:63), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of differe..."

❝ Cite Node

@article{Unknown2026Deep,
  title={Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈94CitationsSemantic Scholar

🏛️86AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

🏷️ Research Topics

speech modelsvision models

📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 94

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