The core of the video surveillance business is data, and the data is the business itself, so based on the big data architecture, it can bring many benefits to medium and large-scale video surveillance projects.
First, the architecture is more flexible and more flexible
For some mid- to large-scale projects, due to the difference in starting point and the lack of top-level design of the video surveillance architecture, later expansion and upgrades are inevitable. For example, the introduction of a big data-oriented architecture at the beginning of the construction will bring benefits to business expansion and management.
Second, cater to the explosive growth of video surveillance data with inexpensive general-purpose hardware
In the big data-oriented architecture, multiple HDFS clusters can be set up according to the deployment needs of the video surveillance service. The collected streaming data will be divided into segments and distributed in data nodes. These data nodes can use inexpensive general-purpose hardware The high reliability is guaranteed by software technology. This method avoids the traditional high-end hardware model and greatly reduces investment costs.
Third, intelligent analysis and data mining through high-speed parallel computing
For gold mines, only shiny gold is valuable. Video surveillance data is like such a gold mine. The traditional manual and serial data screening methods cannot meet the requirements in the era of big data. The principle of big data-oriented architecture is to decompose massive data into smaller and more accessible batches of data, and analyze and process them on multiple servers in parallel, thus greatly speeding up the processing of video data.
Combining the characteristics of the video surveillance business, introducing the Hadoop architecture and building a big data-oriented video surveillance architecture from the perspective of the top-level design will have a profound impact on the planning and design of the future video surveillance business. The following briefly describes the logical architecture of big data video surveillance.
Data source layer, including real-time data and non-real-time data. Real-time data refers to real-time streaming media data generated by IP cameras and sensors. Non-real-time data refers to media data imported from DVRs, encoders, and third-party systems.
Value brought by big data video surveillance architecture
The big data storage layer adopts HDFS and HBASE to realize low-cost and highly reliable management of data. Store the collected video stream in the HDFS cluster and establish an access index through HBase. Reconstruct traditional NVR and dedicated storage into the overall distributed file system.
Big data computing layer to realize intelligent analysis and data mining. Use MapReduce to decompose the analysis of big videos, make full use of idle resources, and hand over computing tasks to multiple servers for parallel computing and analysis. On the other hand, according to the video metadata generated by intelligent analysis, the value of video metadata is mined through Hive information.
Business and management, to achieve equipment and business management. Based on the server cluster composed of Zookeeper, it can ensure the trouble-free operation of the business system, and implement monitoring of cameras and other equipment based on Ganglia.
The video architecture based on big data is essentially a technical architecture that takes video data as the most valuable asset and uses data as the core. It focuses on solving the problems of massive video data dispersion and centralized storage coexistence and multi-level distribution. It greatly improves the efficiency of reading and writing unstructured video data, and provides an end-to-end solution for the rapid retrieval and intelligent analysis of video surveillance.
Value brought by big data video surveillance architecture
Big data video architecture is a revolutionary technology, especially in real-time intelligent analysis and data mining, which makes video surveillance from manual sampling to efficient pre-warning and post-event analysis, to achieve intelligent information analysis and prediction for video surveillance business Bring profound changes.
After video surveillance enters the era of networking, more and more IT emerging technologies are integrated. The broad development path of big data technology in the field of video surveillance has already emerged. Many manufacturers are working to integrate big data technology and video surveillance services. Create video surveillance solutions in the era of big data.
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