THE PROBLEM AND ITS COPE
Since the Stone Age, the human race seeks for strategies to extend its viewing range. With the rise of technology in the twentieth century, cameras are found to be a very useful tool to survey a large area with limited resources. With an increasing numbers of cameras, it becomes more difficult to watch every monitor and prevent incidents in the surveillance area. For the last decades, research seeks for possibilities to automate the process of video surveillance.
Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of video surveillance systems has become a critical requirement. The making of video surveillance systems requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In recent years, with the latest technological advancements, off-the-shelf cameras became vastly available, producing a huge amount of content that can be used in various application areas. Among them, visual surveillance receives a great deal of interest nowadays. Until recently, video surveillance was mainly a concern only for military or large-scale companies. However, increasing crime rate, especially in metropolitan cities, necessitates taking better precautions in security-sensitive areas, like country borders, airports or government offices. Even individuals are seeking for personalized security systems to monitor their houses or other valuable assets. Old-fashioned security systems were vastly relying on human labor instead of system hardware. As a result, detection and assessment of threat was limited with the concentration of the human operator.
Additionally, area under surveillance may be too large to be monitored by a few operators and number of cameras may exceed their monitoring capability. This situation forces the use of more personnel, which makes it even a more expensive task in an era of technological equipment’s’ being much cheaper than the human resource. The sole answer for this increasing demand for personal and societal security is automation. The vast amount of data acquired from video imagery should be analyzed by an intelligent and useful autonomous structure. This intelligent system should have the capacity to observe the surrounding environment and extract useful information for subsequent reasoning, like detecting and analyzing the activity (motion), or identifying the objects entering the scene. Besides, monitoring should be done 24- hours-a-day, without any interruption. This sort of a system will achieve the surveillance task more accurately and effectively, saving a great amount of human effort. In current years, as network bandwidth and computer processing capability and storage capacity to rapidly increase, and various video monitoring information processing technology appearing, video monitoring technology having entered the whole world digitization. With microelectronics, communications and computer technology is developing rapidly, monitoring the traditional approaches have failed to meet the growing market demand...