The video surveillance technology has a wide application prospect in traffic management, public safety, intelligent city, and is developing towards intelligent recognition, real-time processing, and large data analysis. In this paper, we propose a new system for large-scale real-time video surveillance. The system is based on Spark streaming, distributed storage and OLAP framework so that multi-channel video processing has obvious advantages in scalability, fault tolerance and data analysis of the multi-dimensional polymer. According to video processing algorithm, the processing module is divided into single machine processing and distributed processing. The video processing is separated from the data analysis, and the further operation of the multi-channel video output data is completed by using Kafka message queue and Spark streaming. Combining the distributed storage technology with OLAP framework, the system achieves real-time multi-dimensional data analysis and high-performance real-time query.