  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.
