The popularity of camera devices in our life has led to a rapid growth in video data, which contains rich information. Earlier, researchers developed video analytics systems based on traditional Computer Vision techniques to extract and then analyse video data. In recent years, deep learning has made breakthroughs in areas such as face recognition, and novel video analysis systems based on deep learning have appeared. This paper presents an overview of the research progress of novel video analytics systems from the perspectives of applications, technologies and systems. Firstly, this paper reviews the development history of video analytics systems and points out the differences between novel video analytics systems and traditional video analytics systems. Secondly, this paper analyses the challenges of the novel video analysis system in terms of both computation and storage, and discusses the influencing factors of the novel video analysis system in terms of the organisation and distribution of video data and the application requirements of video analysis. This paper then classifies the novel video analytics systems into two categories:those optimised for computation and those optimised for storage, selects typical representatives of these systems and introduces their main ideas. Finally, this paper compares and analyses the novel video analytics systems from multiple dimensions, points out the current problems of these systems, and looks at the future research and development direction of novel video analytics systems accordingly.