Titlebook: Introduction to Intelligent Surveillance; Surveillance Data Ca Wei Qi Yan Textbook 20172nd edition Springer International Publishing AG 201

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Introduction, 2001). Automated calibration of multiple sensors (Zhang, IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334, 2000) will be formatted in mathematical way. In this chapter, we will overview the core issues and demonstrate advanced understanding of the state-of-the-art theories and technologies in intelligent surveillance.

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Surveillance Computing,Runger, Parallel programming for multicore and cluster systems. Springer, Berlin 2010); a few well-known parallel programming models such as OpenMP, UPC, CUDA, etc. have been adopted in practice (Sanders and Kandrot, CUDA by examples: an introduction to general-purpose GPU programming. Addison-Wesley, Upper Saddle River, 2011).

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Surveillance Alarm Making,nytime. Particularly, well-designed algorithms will trigger alarms and reduce the annoying false alarms. In this chapter, we will critically review and justify surveillance alarming algorithms using decision-making approaches so as to save security staff’s human workload.

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Surveillance Data Capturing and Compression,system records monitoring data all day long; to effectively tackle the input data of surveillance systems, technologies of data compression are indispensable which will be detailed at the second half of this chapter.

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转折点

Surveillance Data Capturing and Compression,llance data compression. FSM has been used in the case of transmissions between different states within a system. It is important to study FSM in intelligent surveillance because FSM is an approach to bridge the gap between our real world and semantic space by using events. Moreover, a surveillance