The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Detecting Flying Objects Using a Single Moving Camera
Open accessAbstract
We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We argue that solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approach for object-centric motion stabilization of image patches that allows us to achieve effective classification on spatio-temporal image cubes and outperform state-of-the-art techniques. As this problem has not yet been extensively studied, no test datasets are publicly available. We therefore built our own, both for UAVs and aircrafts, and will make them publicly available so they can be used to benchmark future flying object detection and collision avoidance algorithms.
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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
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Theano: new features and speed improvements
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5590 · Cited by 1,005
Cited by
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research
Taha, B., & Shoufan, A. (2019) · IEEE Access · doi:10.1109/access.2019.2942944 · Cited by 375
Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection
Pawelczyk, M. L., & Wojtyra, M. (2020) · IEEE Access · doi:10.1109/access.2020.3026192 · Cited by 121
Visual-based obstacle detection and tracking, and conflict detection for small UAS sense and avoid
Opromolla, R., & Fasano, G. (2021) · Aerospace Science and Technology · doi:10.1016/j.ast.2021.107167 · Cited by 54
Metadata
- Authors
- Rozantsev, A., Lepetit, V., & Fua, P.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-05-01
- Volume
- 39
- Issue
- 5
- Pages
- 879-892
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2346349911
- Cited by
- 270
- References
- 6
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- green
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