Segmenting the motion components of a video: A long-term unsupervised model

• URL: http://arxiv.org/abs/2310.01040v3
• Date: Wed, 17 Apr 2024 17:44:24 GMT
• Title: Segmenting the motion components of a video: A long-term unsupervised model
• Authors: Etienne Meunier, Patrick Bouthemy,
• Abstract summary: We want to provide a coherent and stable motion segmentation over the video sequence. We propose a novel long-term optical-temporal model operating in a totally unsupervised way. We report experiments on four VOS, demonstrating competitive quantitative results.
• Score: 5.801044612920816
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Human beings have the ability to continuously analyze a video and immediately extract the motion components. We want to adopt this paradigm to provide a coherent and stable motion segmentation over the video sequence. In this perspective, we propose a novel long-term spatio-temporal model operating in a totally unsupervised way. It takes as input the volume of consecutive optical flow (OF) fields, and delivers a volume of segments of coherent motion over the video. More specifically, we have designed a transformer-based network, where we leverage a mathematically well-founded framework, the Evidence Lower Bound (ELBO), to derive the loss function. The loss function combines a flow reconstruction term involving spatio-temporal parametric motion models combining, in a novel way, polynomial (quadratic) motion models for the spatial dimensions and B-splines for the time dimension of the video sequence, and a regularization term enforcing temporal consistency on the segments. We report experiments on four VOS benchmarks, demonstrating competitive quantitative results, while performing motion segmentation on a whole sequence in one go. We also highlight through visual results the key contributions on temporal consistency brought by our method.

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