Marco Tiemann

Visual context identification for privacy-respecting video analytics

With the growing need for privacy-aware and privacy-respecting CCTV systems, it becomes crucial to develop workflows and architectures that can support and enhance privacy protection. Recent advances in image processing enable the automation of many surveillance tasks, increasing the risks of privacy infringements. Fortunately, image processing and pattern recognition techniques can also be used for automatically evaluating the context in which video surveillance takes place, and can therefore be employed for applying context-specific privacy rules. This paper describes how Bag-of-Visual-Words algorithms as well as human tracking and gait analysis cane used for recognizing specific sub-contexts that necessitate the application of particular privacy protection rules in usage contexts such as ambient assisted living, public or workspace surveillance We explain how the data of a multi-modal surveillance system should be handled in order to avoid unnecessary processing of sensitive information through Image Quality Descriptors that will support visual classifications by computing reliability measures relating to the image quality such as noise or problems with respect to ambient conditions.

Dynamic Social and Media Content Syndication for Second Screen

Todays generation of Internet devices has changed how users are interacting with media, from passive and unidirectional users to proactive and interactive. Users can use these devices to comment or rate a TV show and search for related information regarding characters, facts or personalities. This phenomenon is known as second screen. This paper describes SAM, an EU-funded research project that focuses on developing an advanced digital media delivery platform based on second screen interaction and content syndication within a social media context, providing open and standardised ways of characterising, discovering and syndicating digital assets. This work provides an overview of the project and its main objectives, focusing on the NLP challenges to be faced and the technologies developed so far.

SAM: Dynamic and Social Content Delivery for Second Screen Interaction

Social media services offer a wide range of opportunities for businesses and developers to exploit the vast amount of information and user-generated content produced via social media. In addition, the notion of TV second screen usage -- the interleaved usage of TV and smart devices such as smartphones -- appears ever more prominent, with viewers continuously seeking further information and deeper engagement while watching movies, TV shows or event coverage. In this work-in-progress contribution, we present SAM, an innovative platform that combines social media, content syndication and targets second screen usage to enhance media content provisioning and advance the user experience. SAM incorporates modern technologies and novel features in the areas of content management, dynamic social media, social mining, semantic annotation and multi-device representation to facilitate an advanced business environment for broadcasters, content and metadata providers and editors to better exploit their assets and increase revenues.

Data integration, semantic data representation and decision support for situational awareness in protection of critical assets

This paper presents the design and development of a system for data integration, data representation, situational awareness and decision support that has been developed in the EC-co-funded research project MOSAIC. The paper motivates the architecture and describes the data representation model and the developed system components. It discusses the approach for improved situational awareness and decision support as a novel integration of systems developed under the MOSAIC project as deployed for the protection of critical assets as a demonstrator.

Scripting a Smart City: The CityScripts Experiment in Santander

This paper describes a work-in-progress programming experiment where the playground is an entire city. Based on the SmartSantander FIRE infrastructure, the City Script project is aimed at integrating and experimenting a Web of Things scenario in which sensors and actuators in the city have a digital counterpart and can eventually used to compose mashups with social networks and other digital online sources of data.

MOSAIC: Multimodal analytics for the protection of critical assets

This paper presents an overview of the MOSAIC architecture and the validated Demonstrator resulting from an EU-co-funded research project concerned with the development of an advanced system for the use and integration of multimodal analytics for the protection of critical assets. The paper motivates the MOSAIC vision and describes the major components of the integrated solution; including the ontological framework, the data representation, text mining, data mining, video analytics, social network analysis and decision support. In the descriptions of these components, it is illustrated how MOSAIC can be used to improve the protection of critical assets without necessitating data gathering that goes beyond what is already currently being gathered by relevant security organisations such as police forces by improving data analytics techniques, integration of analysis outputs and decision support mechanisms.

MOSAIC: A Multi-modal Surveillance System to Enhance Situation Awareness and Decision Making

With increasing complexity of systems under surveillance, demand grows for automated video-based surveillance systems which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. Traditionally, those systems have been developed based on techniques derived from the fields of image processing and pattern recognition. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a system which aims at exploiting multi-modal data analysis comprising advanced tools for video analytics, text mining, social network analysis, and decision support in order to provide from a richer context an understanding of behaviour of the system under surveillance and to support police personnel in decision making processes.