# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 4 Experimental Result
We found that the MEPE values of the proposed CAFT were 68% superior to HD3 methods, which performed the lowest on the Sintel (train) Clean (C+T) dataset, and 52%, 50%, 45%, 45%, 39%, and 14%, respectively, compared to PWC-Net, LiteFlowNet, LiteFlowNet, LiteFlowNet2, MaskFlowNet, FlowNet, and RAFT. The Sintel (train) Final (C+T) dataset performed 76% better than HD3 and 21% better than RAFT. The Sintel Final dataset is less refined than the Clean dataset and contains data such as noise and low resolution. The proposed method showed the best performance in the results of the Sintel (train) and Final (C+T) data. This result shows that the proposed method works stably even in various environments. In the F1-epe of KITTI-15 (train(C+T), MEPE was the best at 4.87, and HD3 was the lowest at 13.17 (63%). In addition, PWC-Net and LiteFlowNet showed the same 10.39 (53%). LiteFlowNet2, FlowNet2, and RAFT represented 8.97 (46%), 10.08 (52%), and 5.04 (3%), respectively. Also, in F1-all, the proposed method was the best at 12.8, while HD3, which showed the lowest performance, showed 24.0 (47%), and RAFT showed 17.4 (26%). In the experimental results of the model learned using C+T+S+K data, the proposed CAFT showed the best result of 0.62 in Sintel Clean(Train). LiteFlowNet2 showed 1.30 (52%), and RAFT showed 0.76 (18%). In the Sintel Final (Train), the proposed CAFT performed 40% better than LiteFlowNet2 and 20% better than RAFT. In the KITTI-15 Fl-eps(train) and Fl-all data, the proposed CAFT showed the best performance with 0.58 and 1.1, respectively. Sintel Clean and Final (test) showed 1.55 and 2.37, about 20% and 25% better performance than RAFT, respectively. The optical flow results of each tiger and wall scene in the MPI Clean and Final datasets are shown in Figures 14 and 15. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Training Data**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Sintel (Train)**} & \multicolumn{2}{c}{**KITTI-15 (Train)**} & \multicolumn{2}{c}{**Sintel (Test)**} \\ \cline{3-8}  & & **Clean** & **Final** & **F1-epe** & **F1-All** & **Clean** & **Final** & **F1-All** \\ \hline \multirow{8}{*}{C+T} & HD3 [26] & 3.84 (68\%) & 8.77 (76\%) & 13.17 (63\%) & 24.0 (47\%) & - & - & - \\  & PWC-Net [11] & 2.55 (52\%) & 3.93 (46\%) & 10.39 (53\%) & 28.5 (55\%) & - & - & - \\  & LiteFlowNet [13] & 2.48 (50\%) & 4.04 (47\%) & 10.39 (53\%) & 28.5 (55\%) & - & - & - \\  & LiteFlowNet [21] & 2.24 (45\%) & 3.78 (44\%) & 8.97 (46\%) & 25.9 (51\%) & - & - & - \\  & MaskFlowNet [27] & 2.25 (45\%) & 3.61 (41\%) & - & 23.1 (45\%) & - & - & - \\  & FlowNet2 [9] & 2.02 (39\%) & 3.54 (40\%) & 10.08 (52\%) & 30.0 (57\%) & - & - & - \\  & RAFT [16] & 1.43 (14\%) & 2.71 (21\%) & 5.04 (3\%) & 17.4 (26\%) & - & - & - \\  & Proposed CAFT & 1.23 & 2.13 & 4.87 & 12.8 & - & - & - \\ \hline \multirow{8}{*}{C+T+S+K} & LiteFlowNet2 [14] & 1.30 (52\%) & 1.62 (40\%) & 1.47 (61\%) & 4.8 (77\%) & 3.48 (55\%) & 4.69 (49\%) & 7.74 (44\%) \\  & PWC-Net [12] & 1.71 (64\%) & 2.34 (58\%) & 1.5 (61\%) & 5.3 (99\%) & 3.45 (55\%) & 4.60 (48\%) & 7.72 (44\%) \\ \cline{1-1}  & MaskFlowNet [27] & - & - & - & - & 2.52 (38\%) & 4.17 (43) & 6.10 (29\%) \\ \cline{1-1}  & VCN [15] & 1.66 (63\%) & 2.24 (56\%) & 1.16 (50\%) & 4.1 (73\%) & 2.81 (45\%) & 4.40 (46\%) & 6.30 (32\%) \\ \cline{1-1}  & RAFT [16] & 0.76 (18\%) & 1.22 (20\%) & 0.63 (8\%) & 1.5 (27\%) & 1.94 (20\%) & 3.18 (25\%) & 5.10 (15\%) \\ \cline{1-1}  & Proposed CAFT & 0.62 & 0.98 & 0.58 & 1.1 & 1.55 & 2.37 & 4.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optical flow dataset learning results for each model. ‘C + T’ denotes training only on the FlyingChairs and FlyingThings datasets, and ‘C+ S + K + H’ denotes fine-tuning on the combination of FlyingChairs, FlyingThings, Sintel, and KITTI training sets. Figure 13: Comparing parameter counts, inference time, and training iterations vs. accuracy.