By Lin, Weiyao; Verstockt, Steven; Van Hoecke, Sofie; Tilley, Nele; Merci, Bart; Sette, Bart; Lambert, Peter; Hollemeersch, Charles; Van de Walle, Rik
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Jun-Tao & Xiao-Yuan, 2009) proposes performance evaluation models, based on UML collaboration and sequence diagram. , 1998) uses a method which automatically derives a performance evaluation model from a software architecture speciﬁcation. The approach, is interesting but cannot be applied to surveillance systems. , 2009) challenges the evaluation methods based by architectural modelling. The modelling methods (especially for early evaluation) have tendency to be one-dimensional as their main purpose is to avoid implementing systems with poor quality.
Video content analysis is widely deployed in surveillance applications for urban environments, high security objects (usually for access control purposes) and commercial areas. Adding intelligent system tools should have an immediate positive effect on system performance. The system should increase probability of detection. However, the ﬁnal result of applying intelligent system tools might be also negative. The system intelligence applications 20 Video Surveillance operations are scenario- speciﬁc, dependent on data context and their efﬁciency relies on conﬁguration efforts.
It is worth to mention that video content analysis tools should not only have positive impact on system performance but also should have minimum impact on remaining system efﬁciency factors - cost level and general system features. These facts are major motivations towards introducing pre-deployment performance evaluation. The performance of system intelligence mechanisms in general level can be measured by the same metrics and methodologies as the ones proposed for data acquisition systems. In context of intelligent systems one also can use other metrics (combining the same values as precision and recall): (a) the frequency bias, (b) the proportion of correct, (c) probability of detection and (d) false alarm ratio.