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Rethinking RNN-Based Video Object Segmentation

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Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1691))

Abstract

Video Object Segmentation is a fundamental task in computer vision that aims at pixel-wise tracking of one or multiple foreground objects within a video sequence. This task is challenging due to real-world requirements such as handling unconstrained object and camera motion, occlusion, fast motion, and motion blur. Recently, methods utilizing RNNs have been successful in accurately and efficiently segmenting the target objects as RNNs can effectively memorize the object of interest and compute the spatiotemporal features which are useful in processing the visual sequential data. However, they have limitations such as lower segmentation accuracy in longer sequences. In this paper, we expand our previous work to develop a hybrid architecture that successfully eliminates some of these challenges by employing additional correspondence matching information, followed by extensively exploring the impact of various architectural designs. Our experiment results on YouTubeVOS dataset confirm the efficacy of our proposed architecture by obtaining an improvement of about 12pp on YoutTubeVOS compared to RNN-based baselines without a considerable increase in the computational costs.

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Acknowledgement

This work was supported by the TU Kaiserslautern CS PhD scholarship program, the BMBF project ExplAINN (01IS19074), and the NVIDIA AI Lab (NVAIL) program. Further, we thank Christiano Gava, Stanislav Frolov, Tewodros Habtegebrial and Mohammad Reza Yousefi for the many interesting discussions and proofreading of this paper. Finally, we thank all members of the Deep Learning Competence Center at the DFKI for their feedback and support.

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Authors and Affiliations

  1. TU Kaiserslautern, Kaiserslautern, Germany

    Fatemeh Azimi & Andreas Dengel

  2. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Fatemeh Azimi, Federico Raue, Jörn Hees & Andreas Dengel

Authors
  1. Fatemeh Azimi
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  2. Federico Raue
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  3. Jörn Hees
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  4. Andreas Dengel
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Correspondence to Fatemeh Azimi .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Augusto de Sousa

  2. Czech Technical University in Prague, Prague, Czech Republic

    Vlastimil Havran

  3. Mines ParisTech, Paris, France

    Alexis Paljic

  4. Davidson College, Davidson, NC, USA

    Tabitha Peck

  5. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  6. Monash University, Melbourne, Australia

    Helen Purchase

  7. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  8. University of Barcelona, Barcelona, Spain

    Petia Radeva

  9. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

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Azimi, F., Raue, F., Hees, J., Dengel, A. (2023). Rethinking RNN-Based Video Object Segmentation. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2021. Communications in Computer and Information Science, vol 1691. Springer, Cham. https://doi.org/10.1007/978-3-031-25477-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-25477-2_16

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