Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009

Dynamic Evacuation Planning by Visual Analytics-An Expert Survey

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Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues, we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration, hypotheses generation, and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally, users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making, we gather uncertainties introduced by the computer vision step, communicate these information to users through uncertainty visualization, and grant fuzzy hypothesis formulation to interact with the machine. Finally, we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009

Auditory Support for Situation Awareness in Video Surveillance

Presented at the 18th International Conference on Auditory Display (ICAD2012) on June 18-21, 2012 in Atlanta, Georgia.Reprinted by permission of the International Community for Auditory Display, http://www.icad.org.We introduce a parameter mapping sonification to support situational awareness of surveillance operators during their task of monitoring video data. The presented auditory display produces a continuous ambient soundscape reflecting the changes in video data. For this purpose, we use low-level computer vision techniques, such as optical-flow extraction and background subtraction, and rely on the capabilities of the human auditory system for high-level recognition. Special focus is put on the mapping between video features and sound parameters. We optimize this mapping to provide a good interpretability of the sound pattern, as well as an aesthetic non-obtrusive sonification: precision of the conveyed information, psychoacoustic capabilities of the auditory system, and aesthetical guidelines of sound design are considered by optimally balancing the mapping parameters using gradient descent. A user study evaluates the capabilities and limitations of the presented sonification, as well as its applicability to supporting situational awareness in surveillance scenarios.This work was funded by German Research Foundation (DFG) as part of the Priority Program “Scalable Visual Analytics” (SPP 1335)