| People | Locations | Statistics |
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| Tekkaya, A. Erman |
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| Förster, Peter |
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| Mudimu, George T. |
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| Shibata, Lillian Marie |
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| Talabbeydokhti, Nasser |
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| Laffite, Ernesto Dante Rodriguez |
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| Schöpke, Benito |
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| Gobis, Anna |
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| Alfares, Hesham K. |
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| Münzel, Thomas |
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| Joy, Gemini Velleringatt |
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| Oubahman, Laila |
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| Filali, Youssef |
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| Philippi, Paula |
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| George, Alinda |
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| Lucia, Caterina De |
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| Avril, Ludovic |
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| Belachew, Zigyalew Gashaw |
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| Kassens-Noor, Eva | Darmstadt |
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| Cho, Seongchul |
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| Tonne, Cathryn |
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| Hosseinlou, Farhad |
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| Ganvit, Harsh |
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| Schmitt, Konrad Erich Kork |
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| Grimm, Daniel |
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With, Phn Peter De
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2018Semiautomatic training and evaluation of a learning-based vehicle make and model recognition system
- 2018Conditional transfer with dense residual attention: synthesizing traffic signs from street-view imagery
- 2017Fast scene analysis for surveillance & video databases
- 2015Combined generation of road marking and road sign databases applied to consistency checking of pedestrian crossings
- 2015Free-space detection using online disparity-supervised color modeling
- 2015A vision-based approach for tramway rail extraction
- 2014Context-based object-of-interest detection for a generic traffic surveillance analysis system
- 2014Extending the stixel world with online self-supervised color modeling for road-versus-obstacle segmentation
- 2014Optimal performance-efficiency trade-off for bag of words classification of road signs
- 2014Mutation detection for inventories of traffic signs from street-level panoramic images
- 2014Exploiting street-level panoramic images for large-scale automated surveying of traffic sign
- 2013Robust classification system with reliability prediction for semi-automatic traffic-sign inventory systems
- 2012Large-scale classification of traffic signs under real-world conditions
- 2011Nighttime vision-based car detection and tracking for smart road lighting system
Places of action
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document
Mutation detection for inventories of traffic signs from street-level panoramic images
Abstract
Road safety is positively influenced by both adequate placement and optimal visibility of traffic signs. As their visibility degrades over time due to e.g. aging, vandalism, accidents and vegetation coverage, up-to-date inventories of traffic signs are highly attractive for preserving a high road safety. These inventories are performed in a semi-automatic fashion from street-level panoramic images, exploiting object detection and classification techniques. Next to performing inventories from scratch, these systems are also exploited for the efficient retrieval of situation changes by comparing the outcome of the automated system to a baseline inventory (e.g. performed in a previous year). This allows for specific manual interactions to the found changes, while skipping all unchanged situations, thereby resulting in a large efficiency gain. This work describes such a mutation detection approach, with special attention to re-identifying previously found signs. Preliminary results on a geographical area containing about 425 km of road show that 91.3% of the unchanged signs are re-identified, while the amount of found differences equals about 35% of the number of baseline signs. From these differences, about 50% correspond to physically changed traffic signs, next to false detections, misclassifications and missed signs. As a bonus, our approach directly results in the changed situations, which is beneficial for road sign maintenance.
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