Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
Abstract
We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modeling moving objects as arbitrary obstacles, they should be categorized and tracked in order to predict their future behavior. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category-specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects’ trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
In that case, non-parametric methods such as particle filters are used, and physical parameters such as the size of the object are tracked alongside dynamics.
“In that case, non-parametric methods such as particle filters are used, and physical parameters such as the size of the object are tracked alongside dynamics [191].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [191]
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560 · see citing works
Boros, E., & Hammer, P. L. (2002) · Discrete Applied Mathematics · doi:10.1016/s0166-218x(01)00341-9 · Cited by 827 · see citing works
Multiple Target Tracking Using Spatio-Temporal Markov Chain Monte Carlo Data Association
Yu, Q., Medioni, G., & Cohen, I. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.382991 · Cited by 132 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
“Kalman filtering is sufficient for simple linear models, while the extended and and unscented Kalman filters [186] are used to handle nonlinear dynamic models [187].”
Method
- Authors
- Ess, A., Schindler, K., Leibe, B., & Van Gool, L.
- Affiliations
- ETH Zurich (CH)
- Technische Universität Darmstadt (DE)
- RWTH Aachen University (DE)
- Computer Vision Laboratory, ETH Zürich, Zürich, Switzerland and ESAT/PSI-VISICS IBBT, K.U. Leuven, Heverlee, Belgium
- Corresponding author
- Schindler, Konrad
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2010-05-12
- Volume
- 29
- Issue
- 14
- Pages
- 1707-1725
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2041600899
- Cited by
- 191
- References
- 4
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 12 · 2023: 12 · 2022: 8 · 2021: 9 · 2020: 24 · 2019: 19 · 2018: 7 · 2017: 6 · 2016: 11 · 2015: 12 · 2014: 13 · 2013: 20 · 2012: 15
- FWCI
- 34.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The International Journal of Robotics Research (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- object detection, multi-target tracking, autonomous navigation, dynamic environments, semantic navigation, pedestrian detection, car detection, stereo vision, dynamic path planning, trajectory prediction
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
