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What is Temporal Dynamics in Video Analysis about?

This paper introduces a framework for modeling hidden temporal dynamics in video data using deep learning and Gaussian processes, specifically applying it to detect combustion instability in swirl-stabilized combustors. By utilizing a deep Convolutional Neural Network (CNN) for spatial feature extraction and a Gaussian process for temporal dynamics modeling, the approach achieves high detection accuracy with fewer parameters. The results demonstrate improved performance in detecting transitions from stable

Author

제갈현욱

Language

EN