Trajectory Extraction and Deep Features for Classification of Liquid-gas Flow under the Context of Forced Oscillation

: Computer vision and deep learning techniques are increasingly applied to analyze experimental processes in engineering domains. In this paper, we propose a new dataset of liquid-gas flow videos captured from a mechanical model simulating a cooling gallery of an automobile engine, through forced oscillations. The analysis of this dataset is of interest for fluid-mechanic field to validate the simulation environment. From computer vision point of view, it provides a new dynamic texture dataset with challenging tasks since liquid and gas keep changing constantly and the form of liquid-gas flow is closely related to the external environment. In particular predicting the rotation velocity of the engine corresponding to liquid-gas movements is a first step before precise analysis of flow patterns and of their trajectories. The paper also provides an experimental analysis showing that such rotation velocity can be hard to predict accurately. It could be achieved using deep learning approaches but not with state-of-the-art method dedicated to trajectory analysis. We show also that a preprocessing step with difference of Gaussian (DoG) over multiple scales as input of deep neural networks is mandatory to obtain satisfying results, up to 81.39% on the test set. This study opens an exploratory field for complex tasks on dynamic texture analysis such as trajectory analysis of heterogeneous masses.