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

TL;DR

This survey reviews how deep learning algorithms are applied to video analytics in smart cities, covering topics like object detection, tracking, face recognition, and scene labeling to enhance urban data processing.

Contribution

It provides a comprehensive overview of deep learning applications in smart city video analytics, highlighting recent research and developments in the field.

Findings

Deep learning significantly improves accuracy in object detection and tracking.

Face recognition and scene labeling benefit from advanced deep architectures.

The survey identifies key challenges and future directions in smart city video analytics.

Abstract

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.