Machine Learning–Based Online Visual Tracking with Multi-featured Adaptive Kernel Correlation Filter
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47942-7_11
Reference9 articles.
1. Boddeti, V., Kanade, T., & Kumar, B.: Correlation Filters for Object Alignment. Proceedings/CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2291–2298 (2013). https://doi.org/10.1109/CVPR.2013.297
2. Kiani, H., Sim, T., Lucey, S.: Multi-channel Correlation Filters, pp. 3072–3079 (2013). https://doi.org/10.1109/ICCV.2013.381
3. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with Kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2015). https://doi.org/10.1109/TPAMI.2014.2345390
4. Bolme, D., Beveridge, J., Draper, B., Lui, Y.: Visual object tracking using adaptive correlation filters. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010). https://doi.org/10.1109/CVPR.2010.5539960
5. Henriques, J., Carreira, J., Caseiro, R., Batista, J.: Beyond Hard Negative Mining: Efficient Detector Learning Via Block-Circulant Decomposition, pp. 2760–2767 (2013). https://doi.org/10.1109/ICCV.2013.343
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