Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
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Abstract (arXiv)
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 different research topics: object detection, object tracking, face recognition, image classification and scene labeling.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Dec 2015.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1; 2025: 1.
6 papers.
- 2022Why is the video analytics accuracy fluctuating, and what can we do about it?cited by 3
- 2022A Simple Structure For Building A Robust Modelcited by 1
- 2024Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contextscited by 1
- 2021A Statistician Teaches Deep Learningcited by 0
- 2023Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principlecited by 0
- 2025Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Trafficcited by 0
- 2025Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic10 Dec 2025
- 2024Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contexts26 Apr 2024
- 2023Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle12 Jan 2023
- 2022Why is the video analytics accuracy fluctuating, and what can we do about it?23 Aug 2022
- 2022A Simple Structure For Building A Robust Model25 Apr 2022
- 2021A Statistician Teaches Deep Learning29 Jan 2021