Hidden state refinement for optical flow forecasting
Date
2023
Authors
Babenko, Anton
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Abstract
In recent years the topic of optical flow has become well-spread due to computation
power support and optical flow estimation applications used on mobile phones and
edge devices: video editors, frame stabilizations, and autonomous driving feature
providers. This work analyzes multiple approaches to optical flow estimation and
finds the main problems of the optical flow methods: slow convergence and long
execution of the prediction algorithm. We propose to solve the slow convergence
and long execution time with hidden state refinement to provide the initialization
for optical flow estimation based on several previous frames and their hidden state
transformations, which imitates the pixel movement at the hidden state level. The
proposed method uses CNN, LSTM, and Transformer blocks which help to achieve
the optical flow estimation and hidden state refinement to speed up the system. We
used Sintel, KITTY-15, FlyingChairs, FlyingThings, HD1K, DAVIS, and YouTubeVOS datasets for our experiment.
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Citation
Babenko Anton. Hidden state refinement for optical flow forecasting. Master Thesis. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2023, 43 p.