Deep learning-based stereo camera multi-video synchronization
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Abstract (arXiv)
Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparison of different deep learning-based systems and prove that some are efficient and generalizable enough for such a task. This study paves the way to a production ready software-based video synchronization system.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 22 Mar 2023.
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5 of 17 references.
References · 5
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2014Deep metric learning using Triplet networkcited by 421
- 2016Deep Image Retrieval: Learning global representations for image searchcited by 147
- 2016Gated Siamese Convolutional Neural Network Architecture for Human Re-Identificationcited by 34
- 2018Wireless Software Synchronization of Multiple Distributed Camerascited by 12
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