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Big data applications in video surveillance

Video surveillance data have meaning both worlds - the massive and unstructured. Video surveillance massive amount of data, and with the high-definition, ultra Thanh Hoa strengthening trend, video surveillance data size will grow exponentially faster; and generally speaking different structured data, video surveillance data generated by the vast most unstructured data-based, which gives the traditional data management and use of the mechanism has brought great challenges. The rapid growth of video surveillance data, the traditional video surveillance system architecture, data management, data analysis applications face a new dilemma. Due to the rapid expansion of demand for the amount of data, and the consequent large-scale computing, more and more blindly adopt high with hardware, making hardware investment has become not shirk customers, customers increasingly want to meet the needs of the premise, with low-end hardware to replace high with hardware. Camera lens cover weather record everything that happens within the range of valid information may only be distributed in a relatively short period of time, often the greater the higher the density of information on customer value. After the video surveillance business network, more and more devices within the network, the use of idle computing resources, and maximize the use of resources related to the efficiency of operations. In the field of video surveillance, video analysis often determine the value of efficiency, lower latency, more accurate analysis is often widespread demand safe city such customers. As the amount of data increases, even for TB-level data for video content analysis and retrieval of data, using serial computing model may need to spend hours of computing, is far from competent timeliness requirements. Video analysis and retrieval, can not rely on traditional means, a huge amount of data to optimize the efficiency of parallel computing is the only way video intelligence analysis.
Typical network video surveillance data storage model only way to go is to add up in big data technology support, network video surveillance data storage model steerable distributed data storage system to provide efficient, safe, inexpensive storage . In the video surveillance business, wrong missed watching, too late to see common problems. Big Data surveillance images back to many security monitoring and management staff brings multiple challenges. Big data trend in video surveillance has come on the occasion, rely on the human eye to retrieve, view all the video image data has not realistic. Video images through the big data technology fuzzy query, rapid retrieval, accurate positioning, so look easy imminent. With video surveillance cameras covering the breadth, density increases, the amount of video data increased exponentially, and the efficient use of video surveillance data on the decline. Intelligent transportation applications, consumer behavior analysis applications, such as integrated video surveillance and business intelligence analysis image appears is trying to break through the video surveillance efficiency values and low commercial value bottleneck.
Core business is video surveillance data, is the business itself, big data architecture can bring many benefits to the large-scale video surveillance projects. For some of the larger projects, due to differences in the starting point, the lack of video surveillance architecture of top-level design, expansion and upgrade late inevitably unwieldy, such as the introduction at the beginning of the construction of a large data-oriented architecture, benefits for business expansion and management. Cheap generic hardware to meet the explosive growth of video surveillance data. In large data-oriented architecture, according to business video surveillance deployments require the establishment of multiple HDFS clusters composed of streaming data collected will be divided into segments and distributed data nodes, the nodes can use these data types cheap generic hardware to ensure high reliability software technology, this approach avoids the traditional high-end hardware model, greatly reducing the cost of investment. Through high-speed parallel computing intelligent analysis and data mining. Traditional manual and serial data screening methods can not meet the requirements in the era of big data. Oriented Architecture principles of big data is to massive data into smaller batches of data more accessible on multiple servers in parallel analytical processing, thus greatly speeding up the process of video data processing....

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