The popularity of camera devices in daily 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 to analyze 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, the development history of video analytics systems is reviewed and the differences are pointe out between novel video analytics systems and traditional video analytics systems. Secondly, the challenges of the novel video analysis system are analyzed in terms of both computation and storage, and the influencing factors of the novel video analysis system are discussed in terms of the organization and distribution of video data and the application requirements of video analysis. Then, the novel video analytics systems are classified into two categories: Optimized for computation and optimized for storage, typical representatives of these systems are selects and their main ideas are introduced. Finally, the novel video analytics systems are compared and analyzed from multiple dimensions, the current problems of these systems are pointed out, and the future research and development direction of novel video analytics systems are looked at accordingly.