Novel CNN approach for video prediction based on FitVid

Taiju Watanabe; Shindo Takahiro; Hiroshi Watanabe

doi:10.1117/12.2665224

25 March 2023 Novel CNN approach for video prediction based on FitVid

Taiju Watanabe, Shindo Takahiro, Hiroshi Watanabe

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 1259210 (2023) https://doi.org/10.1117/12.2665224
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

Video prediction is a task in computer vision that predicts future frames from the past few frames of video. In video prediction, a simple CNN-based approach called SimVP has marked remarkable performance without using RNN or vison transformer (ViT). In this paper, we propose a model structure to improve performance of video prediction based on FitVid. FitVid is a regression-based method of predicting future videos using not only video but also motion. We focus on video prediction only conditioned on videos. To this goal, we introduce network modules used in SimVP to FitVid. Experimental results show that the proposed structure shows better prediction accuracy compared to SimVP.

Citation Download Citation

Taiju Watanabe, Shindo Takahiro, and Hiroshi Watanabe "Novel CNN approach for video prediction based on FitVid", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 1259210 (25 March 2023); https://doi.org/10.1117/12.2665224

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KEYWORDS

Video

Performance modeling

Computer programming

Motion models

Data modeling

Education and training

Interpolation

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