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FAR: Fourier Aerial Video Recognition

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

We present an algorithm, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background. Our disentanglement technique operates in the frequency domain to characterize the extent of temporal change of spatial pixels, and exploits convolution-multiplication properties of Fourier transform to map this representation to the corresponding object-background entangled features obtained from the network. To encapsulate contextual information and long-range space-time dependencies, we present a novel Fourier Attention algorithm, which emulates the benefits of self-attention by modeling the weighted outer product in the frequency domain. Our Fourier attention formulation uses much fewer computations than self-attention. We have evaluated our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night, Drone Action, and NEC Drone. We demonstrate a relative improvement of 8.02%–38.69% in top-1 accuracy and up to 3 times faster over prior works.

The second and third authors contributed equally.

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Acknowledgements

We thank Rohan Chandra for reviewing the paper. This research has been supported by ARO Grants W911NF1910069, W911NF2110026 and Army Cooperative Agreement W911NF2120076.

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Correspondence to Divya Kothandaraman .

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Kothandaraman, D., Guan, T., Wang, X., Hu, S., Lin, M., Manocha, D. (2022). FAR: Fourier Aerial Video Recognition. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13697. Springer, Cham. https://doi.org/10.1007/978-3-031-19836-6_37

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