Abstract
Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.
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Acknowledgments
This work has been supported by SSF (SymbiCloud), VR (EMC\({}^2\), starting grant 2016-05543), SNIC, WASP, and Nvidia.
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Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M. (2017). DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_5
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DOI: https://doi.org/10.1007/978-3-319-64689-3_5
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