The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A fast, modular scene understanding system using context-aware object detection
Abstract
We propose a semantic scene understanding system that is suitable for real robotic operations. The system solves different tasks (semantic segmentation and object detections) in an opportunistic and distributed fashion but still allows communication between modules to improve their respective performances. We propose the use of the semantic space to improve specific out-of-the-box object detectors and an update model to take the evidence from different detection into account in the semantic segmentation process. Our proposal is evaluated with the KITTI dataset, on the object detection benchmark and on five different sequences manually annotated for the semantic segmentation task, demonstrating the efficacy of our approach.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cadena et al. use motion estimation and interconnect a coarse semantic segmentation with different object detectors to outperform the individual systems.
“More recently, Cadena et al. [26] use motion estimation, and interconnect a coarse semantic segmentation with different object detectors to outperform the individual systems.”
From Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016) · cites this paper as [26]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Manhattan scene understanding using monocular, stereo, and 3D features
Flint, A., Murray, D., & Reid, I. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126501 · Cited by 132
Self-citation
Flagged first, then by citations
Research works printed but not linked: 30
- yao, 2012, Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 702
- 10.1109/ICCV.2009.5459249 · DOI
- wu, 2014, Hierarchical semantic labeling for task-relevant rgb-d perception, Robotics Science and Systems (RSS), 10.15607/RSS.2014.X.006 · DOI
- 10.1109/CVPR.2012.6248074 · DOI · Paper page
- geiger, 2011, Joint 3d estimation of objects and scene layout, Advances in neural information processing systems
- heitz, 2009, Cascaded classification models: Combining models for holistic scene understanding, Advances in Neural Information Processing Systems 21, 641
- heitz, 2008, Learning spatial context: Using stuff to find things, Computer Vision ECCV 2008, 30, 10.1007/978-3-540-88682-2_4 · DOI
- 10.1109/ICCV.2005.107 · DOI
- 10.1007/s11263-008-0137-5 · DOI
- 10.1063/1.1689000 · DOI
- kwitt, 2012, Scene recognition on the semantic manifold, Computer Vision-ECCV 2012, 359, 10.1007/978-3-642-33765-9_26 · DOI
- ladický, 2010, What, Where and How Many? Combining Object Detectors and CRFs, 10.1007/978-3-642-15561-1_31 · DOI
- 10.1109/CVPR.2013.405 · DOI
- 10.1145/1961189.1961199 · DOI · Paper page
- 10.1109/ICRA.2013.6630632 · DOI
- cadena, 2013, Recursive Inference for Prediction of Objects in Urban Environments, International Symposium on Robotics Research
- 10.1109/TPAMI.2014.2300479 · DOI
- wojek, 2008, A dynamic conditional random field model for joint labeling of object and scene classes, Computer Vision ECCV 2008, 733, 10.1007/978-3-540-88693-8_54 · DOI
- dollár, 0, Piotr's Image and Video Matlab Toolbox (PMT)
- 10.1109/TPAMI.2012.231 · DOI · Paper page
- 10.1109/ICRA.2014.6907237 · DOI
- 10.1109/TPAMI.2009.167 · DOI · Paper page
- berger, 1985, Statistical decision theory and Bayesian analysis, 10.1007/978-1-4757-4286-2 · DOI · Paper page
- li, 2010, Towards holistic scene understanding: Feedback enabled cascaded classification models, Advances in neural information processing systems, 23, 1351
- 10.1109/CVPR.2014.119 · DOI · Paper page
- 10.1177/0278364912437903 · DOI
- 10.1109/CVPR.2008.4587372 · DOI
- 10.1007/s11263-011-0514-3 · DOI
- scharwächter, 2014, Stixmantics: A medium-level model for real-time semantic scene understanding, Computer Vision-ECCV 2014, 533, 10.1007/978-3-319-10602-1_35 · DOI
- 10.1109/CVPR.2013.396 · DOI
Cited by
Sorted by citations
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Self-citation
Metadata
- Authors
- Cadena, C., Dick, A., & Reid, I. D.
- Affiliations
- The University of Adelaide (AU)
- Venue
- 2015 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 4859-4866
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1593286969
- Cited by
- 29
- References
- 1
- Citations by year
- 2024: 1 · 2023: 5 · 2022: 3 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 7 · 2016: 5 · 2015: 1
- FWCI
- 1.91
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- scene understanding, semantic segmentation, modular robots, distributed computing, semantic space, evidence combination, multi-target detection, KITTI dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
