| Summary: | In the glass wool industry, the molten glass flow is monitored for regulation purposes. Given the progress in the computer vision field, the current monitoring solution might be replaced by a camera based solution. The aim of this thesis is to investigate the possibility of using optical flow techniques for estimation of the molten glass flow displacement. Three glass melt flow datasets were recorded, as well as two additional melt flow datasets, using a NIR camera. The block matching techniques Full Search (FS) and Adaptive Rood Pattern Search (ARPS), as well as the local feature methods ORB and A-KAZE were considered. These four techniques were compared to RAFT, the state-of-the-art approach for optical flow estimation, using available pre-trained models, as well as an approach of using the tracking method ECO for the optical flow estimation. The methods have been evaluated using the metrics MAE, MSE, and SSIM to compare the warped flow to the target image. In addition, ground truth for 50 frames from each dataset was manually annotated as to use the optical flow metric End-Point Error. To investigate the computational complexity the average computational time per frame was calculated. The investigation found that RAFT does not perform well on the given data, due to the large displacements of the flows. For simulated displacements of up to about 100 pixels at full resolution, the performance is satisfactory, with results comparable to the traditional methods. Using ECO for optical flow estimation encounters similar problems as RAFT, where the large displacement proved challenging for the tracker. Simulating smaller motions of up to 60 pixels resulted in good performance, though computation time of the used implementation is much too high for a real-time implementation. The four traditional block matching and local feature approaches examined in this thesis outperform the state-of-the-art approaches. FS, ARPS, A-KAZE, and ORB all have similar performance on the glass flow datasets, whereas the block matching approaches fail on the alternative melt flow data as the template extraction approach is inadequate. The two local feature approaches, though working reasonably well on all datasets given full resolution, struggle to identify features on down-sampled data. This might be mitigated by fine-tuning the settings of the methods. Generally, ORB mostly outperforms A-KAZE with respect to the evaluation metrics, and is considerably faster.
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