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

arXiv: https://arxiv.org/abs/1512.03131 · HTML: https://mlsanity.com/paper/1512.03131/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2015.

## Built on by (most cited first, 6 of 6)

- [Why is the video analytics accuracy fluctuating, and what can we do about it?](https://mlsanity.com/paper/2208.12644/) (2022)
- [A Simple Structure For Building A Robust Model](https://mlsanity.com/paper/2204.11596/) (2022)
- [Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contexts](https://mlsanity.com/paper/2404.17212/) (2024)
- [A Statistician Teaches Deep Learning](https://mlsanity.com/paper/2102.01194/) (2021)
- [Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle](https://mlsanity.com/paper/2301.05099/) (2023)
- [Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic](https://mlsanity.com/paper/2512.11898/) (2025)

## 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.