The forensic investigation of a terrorist attack poses a significant challenge to the investigative authorities, as often several thousand hours of video footage must be viewed. Large scale Video Analytic Platforms (VAP) assist law enforcement agencies (LEA) in identifying suspects and securing evidence. Current platforms focus primarily on the integration of different computer vision methods and thus are restricted to a single modality. We present a video analytic platform that integrates visual and audio analytic modules and fuses information from surveillance cameras and video uploads from eyewitnesses. Videos are analyzed according their acoustic and visual content. Specifically, Audio Event Detection is applied to index the content according to attack-specific acoustic concepts. Audio similarity search is utilized to identify similar video sequences recorded from different perspectives. Visual object detection and tracking are used to index the content according to relevant concepts. Innovative user-interface concepts are introduced to harness the full potential of the heterogeneous results of the analytical modules, allowing investigators to more quickly follow-up on leads and eyewitness reports.

This paper presents a system for Braille learning support using real-time panoramic views generated from the novel smart panorama camera 360SCAN. The system makes use of the modern image processing libraries and state-of-the-art features extraction and clustering methods. We compare the real-time frames recorded by the bio-inspired camera to the reference images in order to determine particular figures. One contribution of the proposed method is that image edges can be transformed to the presentation on Braille display directly without any image processing. It is possible due to the bio-inspired construction of camera sensor. Another contribution is that our approach provides Braille users with images recorded from natural scenes. We conducted several experiments that verify the methods that demonstrate learning figures captured by the smart camera. Our goal is to process such images and present them on the Braille Display in a form appropriate for visually impaired people. All evaluations were performed in the natural environment with ambient illumination of 200 lux, which demonstrates high camera reliability in difficult light conditions. The system can be optimized by applying additional filters and features algorithms and by decreasing the rotational speed of the camera. The presented Braille learning support system is a building block for a rich and qualitative educational system for the efficient information transfer focused on visually impaired people.

This paper presents a system for quality control of real-time panoramic views generated from the novel smart panorama camera 360SCAN. The system makes use of the modern image processing library OpenIMAJ and state-of-the-art features extraction and clustering methods. We compare a real-time frame collection recorded by the camera to a reference image collection in order to determine camera readiness. We conducted several experiments that verify the methods that demonstrate smart camera operational status and evaluate changes in the position or number of objects in the working location. All evaluations were performed in the natural environment with ambient illumination of 200 lux, which demonstrates high camera reliability in difficult light conditions. The system can be optimized for embedded applications by applying additional filters and features algorithms and by decreasing the rotational speed of the camera. The presented quality control system is a building block for a rich and qualitative expert system for the efficient control and support of the smart camera.

Current multimedia database systems largely focus on specific media types and/or application domains. This paper gives a sketch of METIS, a database solution for the unified management of any kind of digital media, characterized by profound customizability for domain or application needs. The system core is based on an expressive data model that can be adapted to any scheme for media management, description, and classification desired, and can accommodate arbitrary query operators, similarity measures, and/or feature extraction algorithms. METIS is based on a persistence abstraction layer allowing the exchange of storage back-ends and includes a customizable web front-end for media management. Thereby, METIS establishes a common database foundation for a wide spectrum of multimedia applications.