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. 2022 May 24;9:787627. doi: 10.3389/fcvm.2022.787627

Figure 12.

Figure 12

Comparison of motion tracking error of 5 different GPU-accelerated motion tracking algorithms (Lucas-Kanade, TV-L1, Farnebäck, NVIDIA, and Brox). The error was determined using synthetically generated optical mapping recordings with ground truth displacement data obtained in simulations. The error is measured as the average end-to-end point error (EPE) between the estimated (tracked) optical flow vector field and the ground-truth optical flow vector field. (A) Tracking error (EPE) increases with noise standard deviation σ. While all algorithms are sensitive to noise, the NVIDIA algorithm is very sensitive to noise and quickly produces larger (than sub-pixel) errors, even at relatively low noise levels. The Farnebäck algorithm is the most robust algorithm against noise. (B) Increase in tracking error (EPE error) with increasing fluorescence strength ΔF/F[%]. (C) Compensation for steep increase in tracking (EPE) error with increasing fluorescence strength ΔF/F[%] when tracking contrast-enhanced versions of the original videos shown in (B).