Collaborative Video Surveillance for Distributed Visual Data Mining of Potential Risk and Crime Detection

Collaborative Video Surveillance for Distributed Visual Data Mining of Potential Risk and Crime Detection

Chia-Hui Wang (Ming Chuan University, Taiwan), Ray-I Chang (National Taiwan University, Taiwan) and Jan-Ming Ho (Academia Sinica, Taiwan)
DOI: 10.4018/978-1-61350-101-6.ch313
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Thanks to fast technology advancement of micro-electronics, wired/wireless networks and computer computations in past few years, the development of intelligent, versatile and complicated video-based surveillance systems has been very active in both research and industry to effectively enhance safety and security. In this chapter, the authors first introduce the generations of video surveillance systems and their applications in potential risk and crime detection. For effectively supporting early warning system of potential risk and crime (which is load-heavy and time-critical), both collaborative video surveillance and distributed visual data mining are necessary. Moreover, as the surveillance video and data for safety and security are very important for all kinds of risk and crime detection, the system is required not only to data protection of the message transmission over Internet, but also to further provide reliable transmission to preserve the visual quality-of-service (QoS). As cloud computing, users do not need to own the physical infrastructure, platform, or software. They consume resources as a service, where Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), and pay only for resources that they use. Therefore, the design and implementation of an effective communication model is very important to this application system.
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Video surveillance services have been active for decades to protect lives and properties of individuals, enterprises and governments such as homeland security, office-building security and traffic surveillance on highways. Video surveillance systems have evolved to the third generation (Fong & Hui, 2001; Liang & Yu, 2001; Marcenaro, et al., 2001). In the third-generation systems as shown in Figure 1, all applied devices and technologies are digital. The digital camera can further compress the video data to save the bandwidth for providing users ubiquitous video surveillance services through the prevalent Internet (Ho, et al., 2000). Therefore, we can aggregate different surveillance information from different cameras to provide users more value-added surveillance services (Fong & Hui, 2001; Liang & Yu, 2001; Juang & Chang, 2007) such as fire accident, flood disaster, and debris flow. More details are presented later in this book chapter.

Figure 1.

Basic architecture of the third generation surveillance systems

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