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 it...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Maine
1969-12-01
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| Series: | Journal of Spatial Information Science |
| Subjects: | |
| Online Access: | http://josis.org/index.php/josis/article/view/23 |
| _version_ | 1818933350888374272 |
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| author | Markus Höferlin Benjamin Höferlin Daniel Weiskopf Gunther Heidemann |
| author_facet | Markus Höferlin Benjamin Höferlin Daniel Weiskopf Gunther Heidemann |
| author_sort | Markus Höferlin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T04:46:59Z |
| format | Article |
| id | doaj.art-6214938dd18a4ef883fbea73b724bfe8 |
| institution | Directory Open Access Journal |
| issn | 1948-660X |
| language | English |
| last_indexed | 2024-12-20T04:46:59Z |
| publishDate | 1969-12-01 |
| publisher | University of Maine |
| record_format | Article |
| series | Journal of Spatial Information Science |
| spelling | doaj.art-6214938dd18a4ef883fbea73b724bfe82022-12-21T19:52:57ZengUniversity of MaineJournal of Spatial Information Science1948-660X1969-12-01201128711710.5311/JOSIS.2010.2.120Uncertainty-aware video visual analytics of tracked moving objectsMarkus Höferlin0Benjamin Höferlin1Daniel Weiskopf2Gunther Heidemann3VISUS, Universität StuttgartIntelligent Systems Group, Universität StuttgartVISUS, Universität StuttgartIntelligent Systems Group, Universität StuttgartVast 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.http://josis.org/index.php/josis/article/view/23visual analyticsvideo analysisuncertaintytrajectoriesinteractive queryvideo processingvideo visualizationvideo surveillance |
| spellingShingle | Markus Höferlin Benjamin Höferlin Daniel Weiskopf Gunther Heidemann Uncertainty-aware video visual analytics of tracked moving objects Journal of Spatial Information Science visual analytics video analysis uncertainty trajectories interactive query video processing video visualization video surveillance |
| title | Uncertainty-aware video visual analytics of tracked moving objects |
| title_full | Uncertainty-aware video visual analytics of tracked moving objects |
| title_fullStr | Uncertainty-aware video visual analytics of tracked moving objects |
| title_full_unstemmed | Uncertainty-aware video visual analytics of tracked moving objects |
| title_short | Uncertainty-aware video visual analytics of tracked moving objects |
| title_sort | uncertainty aware video visual analytics of tracked moving objects |
| topic | visual analytics video analysis uncertainty trajectories interactive query video processing video visualization video surveillance |
| url | http://josis.org/index.php/josis/article/view/23 |
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