The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Potential of radar for static object classification using deep learning methods
Abstract
A semantic representation of the environment is extremely beneficial in enabling autonomous driving. The objects in the vehicle's surroundings must be recognized and assigned to a proper class. Redundancy is often required for safety-relevant application. This cannot be provided by a single sensor system. This study now demonstrates the potential of radar-based object recognition using deep learning methods.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Using occupancy grids for mobile robot perception and navigation
Elfes, A. (1989) · Computer · doi:10.1109/2.30720 · Cited by 2,287
Maurer, M., Gerdes, J. C., Lenz, B., & Winner, H. (2015) · Springer eBooks · doi:10.1007/978-3-662-45854-9 · Cited by 161
Detection and extraction of buildings from interferometric SAR data
Gamba, P. E., Houshmand, B., & Saccani, M. (2000) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.823956 · Cited by 142
Instantaneous ego-motion estimation using multiple Doppler radars
Kellner, D., Barjenbruch, M., Klappstein, J., et al. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907064 · Cited by 136
Automotive radar gridmap representations
Werber, K., Rapp, M., Klappstein, J., Hahn, M., Dickmann, J., Dietmayer, K., & Waldschmidt, C. (2015) · IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) · doi:10.1109/icmim.2015.7117922 · Cited by 88
Self-citationObstacle detection and classification fusing radar and vision
Bertozzi, M., Bombini, L., Cerri, P., et al. (2008) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2008.4621304 · Cited by 85
Fast connected component labeling algorithm using a divide and conquer technique.
Park, J.-M., Looney, C. G., & Chen, H.-C. (2000) · Computers and Their Applications · Cited by 71
Detection of parked vehicles from a radar based occupancy grid
Dubé, R., Hahn, M., Schütz, M., et al. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856568 · Cited by 59
Self-citationCLASSIFICATION TREE BASED BUILDING DETECTION FROM LASER SCANNER AND AERIAL IMAGE DATA
Matikainen, L., Kaartinen, H., & Hyyppa, J. M. (2007) · Cited by 54
Vehicle detection and car park mapping using laser scanner
Keat, C. T. M., Pradalier, C., & Laugier, C. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545233 · Cited by 35
Pedestrian recognition in automotive radar sensors
Heuel, S., & Rohling, H. (2013) · tub.dok (Hamburg University of Technology) · Cited by 23
Semi-Markov Process Based Localization Using Radar in Dynamic Environments
Rapp, M., Hahn, M., Thom, M., Dickmann, J., & Dietmayer, K. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.77 · Cited by 23
Self-citationDetection of arbitrarily rotated parked cars based on radar sensors
Lombacher, J., Hahn, M., Dickmann, J., & Wöhler, C. (2015) · International Radar Symposium · doi:10.1109/irs.2015.7226281 · Cited by 18
Self-citationHow do traffic signs look like in radar?
Werber, K., Barjenbruch, M., Klappstein, J., et al. (2014) · European Microwave Conference (EuMC) · doi:10.1109/eumc.2014.6986388 · Cited by 10
RoughCough — A new image registration method for radar based vehicle self-localization
Werber, K., Barjenbruch, M., Klappstein, J., et al. (2015) · Cited by 6
Radar frequency band invariant pedestrian classification
Molchanov, P., Vinel, A. V., Astola, J. T., et al. (2013) · International Radar Symposium · Cited by 4
Research works printed but not linked: 1
- japkowicz, 2000, The Class Imbalance Problem: Significance and Strategies, Proceedings of the 2000 International Conference on Artificial Intelligence, 111
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Sorted by citations
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425
Metadata
- Authors
- Lombacher, J., Hahn, M., Dickmann, J., & Wohler, C.
- Affiliations
- Daimler (Germany) (DE)
- ORCID
- Lombacher, Jakob
- Venue
- 2016 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM)
- Publisher
- IEEE
- Published
- 2016-05
- Volume
- 2016
- Pages
- 1-4
- OpenAlex
- W2513079078
- Cited by
- 76
- References
- 21
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 12 · 2022: 9 · 2021: 19 · 2020: 12 · 2019: 11 · 2018: 3 · 2017: 5
- FWCI
- 11.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) (publishedVersion)
- IEEE Conference Proceedings
- Funders
- European Commission
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Radar Systems and Signal Processing
- Keywords
- radar, autonomous driving, object recognition, deep learning, sensor redundancy, multi-sensor fusion
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