Camera surveillance and recognition of deviant behavior is important for the prevention of criminal incidents. A single observation of subtle deviant behavior of an individual may sometimes be insufficient to merit a follow-up action. Therefore, we propose a method that can combine multiple weak observations to make a strong indication that an intervention is required. We analyze the effectiveness of combining multiple observations/tags of different operators, the effects of the tagging instruction these operators received (many tags for weak signals or few tags for strong signals), and the performance of using a semi-automatic system for combining the different observations. The results show that the method can be used to increase hits (detecting criminals) whilst reducing false alarms (bothering innocent passers-by).
This literature review is aimed at examining state of the art research in the field of online social networks. The goal is to identify the current challenges within this area of research, given the questions raised in society. In this review we pay attention to three aspects of social networks: actor, message, and network characteristics. We further limit our review to research based on Twitter data, because this online social network is the most widely used by researchers in the field.
Surveillance for security requires communication between systems and humans, involves behavioural and multimedia research, and demands an objective benchmarking for the performance of system components. Metadata representation schemes are extremely important to facilitate (system) interoperability and to define ground truth annotations for surveillance research and benchmarks. Surveillance places specific requirements on these metadata representation schemes. This paper offers a clear and coherent terminology, and uses this to present these requirements and to evaluate them in three ways: their fitness in breadth for surveillance design patterns, their fitness in depth for a specific surveillance scenario, and their realism on the basis of existing schemes. It is also validated that no existing metadata representation scheme fulfils all requirements. Guidelines are offered to those who wish to select or create a metadata scheme for surveillance for security.
In this paper the influence of interventions on Twitter users is studied. We define influence in a) number of participants, b) size of the audience, c) amount of activity, and d) reach. Influence is studied for four different target groups: a) politicians, b) journalists, c) employees and d) the general public. Furthermore, two types of interventions are studied: a) by all Twitter users (i.e., uncontrolled interventions), and b) those tweeted by an organization that benefits from any resulting influence (i.e., controlled interventions). As a case study, tweets about a large Dutch governmental organization are used.Results show a clear relation between the number of uncontrolled interventions and influence in all four target groups, for each of the defined types of influence. Controlled interventions show less influence: Significant influence was found for the general public, but influence for politicians and employees was only mildly significant, and no influence was found for journalists. The effect found for uncontrolled interventions however suggests that this influence is indeed reachable for some target groups, even when the number of interventions is small, and very well reachable for all target groups, provided the number of interventions is large enough.In addition to this we found that interventions influence groups to a different extent. Own employees were influenced strongest, differing significantly from the other groups.
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