Bank ATM monitoring scheme based on image recognition technology

1 Development of domestic bank ATM

Due to the special role of ATM (AutomaTIc Teller Machine, hereinafter referred to as ATM) in the banking business, by the end of 2007, the number of ATMs in China has rapidly increased to about 138,000 units, of which about 123,000 ATMs are connected. In China's ATM sales market, the ATM market share of six major banks such as Industrial and Commercial Bank of China, Agricultural Bank of China, Bank of China, China Construction Bank, Bank of Communications, and China Postal Savings Bank accounted for about 85% of the total ATM market in China. However, from the ATM quantity index per million people, the gap in China's ATM demand is still large. In 2006, China had only 73 ATMs per million people, and the city had 273 ATMs per million people, far below the average of 1,150 in the world's top 20 countries. By 2009, the number of ATMs in China has reached 184,000, with an average annual growth rate of over 20%, ranking second in the world after the United States, Japan, and Brazil. According to the current growth rate, some experts predict that In 2011, China's ATM machine capacity will surpass Brazil, ranking third in the world.

2 Bank ATM existing monitoring methods

At present, the monitoring methods of ATMs in domestic banks are mainly due to differences in regional economic conditions and security construction standards. Even if the same bank has different security construction concepts in different regions, the main monitoring methods are mainly the following three.

2.1 Integrating ATM into the entire network monitoring

This type of method is mainly used for ATM monitoring in the line. The ATM type of the line type is installed in a wall-mounted or lobby type. Through-wall ATMs have a flexible and high-grade banknote installation, so these ATM machines are installed in a flexible way on the camera wiring. These ATM machines are usually installed in a uniform room with customer area and banknotes. Area. In addition to installing the cameras required for basic monitoring, each camera needs to be equipped with 1-2 panoramic cameras in the ATM customer hall to monitor the hall in the same way as shown in Figure 1.

2 .2 Independent ATM monitoring method

This method is used for the monitoring of lobby-type ATM models. Due to the limitation of the installed position of this type of ATM machine, the DVR is often miniaturized and installed in an equipment box in the light box at the same time as the ATM. The layout is basically inside the ATM machine and its ancillary equipment. Because of its flexible installation, it can better meet the installation of not only the lobby ATM, but also the installation of the through-wall ATM machine, as shown in Figure 2. The lobby-style ATM also has a monitoring mode that uses a dedicated monitoring host and a network monitoring host to pay their respective responsibilities, as shown in Figure 3.

2.3 ATM video surveillance networking

With the development of product technology, the network function and video compression technology of monitoring equipment are constantly matured and improved, especially based on the application of MPEG4 and H.264 compression technology. The network bandwidth of CIF (352 & TImes; 288) quality is not guaranteed. Under the premise of change, each code stream can be as low as 384 kbit·s-1, or even lower, so that the remote video surveillance network based on the low-bandwidth bank private network or VPDN network is also in the major banks and commercial banks across the country. Large-scale construction and putting into use, these monitoring centers have changed the way banks guard against ATM, from the original passive to the semi-active way, turning the original after-the-fact response into an incident and afterwards, through the image Remote real-time monitoring, the central attendant monitors the ATM site environment to achieve artificial proactive warning.

3 Picture difference detection method

First, background modeling and foreground extraction are performed. Using the mathematical statistical model, the statistical information of many pictures is saved into the model, and then the current picture is compared with the model to extract the place where foreign matter may exist. The background of the method can reach the statistical value of pictures for several hours or even one day, which enhances the adaptability of the detection function to various environments, greatly reduces the false positives, and also reduces the influence of light, while modeling and foreground in the background. Based on the extraction method, some object feature judgments are added to reduce false positives and improve the accuracy of detection. The detection process is shown in Figure 4.




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