# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 6 Conclusions  
In this paper, we propose a cross-end-based optical flow estimation network. The proposed CAFT uses only a single high-speed flow field and has a cross-stage structure that can reduce the number of parameters and increase accuracy. This structure enables fast optical flow calculations with a smaller number of parameters compared to other traditional optical flow techniques. Also, the proposed method applies a network-based cross-stage technique with an encoder and decoder and improves accuracy by connecting the input of the previous layer to the output stage. The proposed method has a 27.5% smaller number of parameters than RAFT, and the optical flow calculation speed is improved by more than 70%. To evaluate the performance of the proposed model, the Flying Chairs (C dataset), Flying Things 3D (T dataset), Sintel (Clean, Final), and KITTI datasets were used. The C and T (Train) datasets were used for learning the proposed network, and the Sintel dataset and KITTI-15 were used for both training and testing.  
In the model using C+T learning data, the proposed CAFT on Sintel (train) data were about 68% better than HD3 and about 14% better than the existing best RAFT.  
Additionally, Sintel Final(train) showed 76% improved performance compared to HD3 and 21% better than RAFT. In the KITTI-15 (Train) data experiment, F1-epe and F1-all showed results of 4.87 and 12.8, respectively, and the performance was superior to other traditional methods.  
In the model using C+T+S+K learning data, the experimental results of Sintel (test) and KITTI-15 (test) showed 1.55, 2.37, and 4.31, respectively, with results improved by 20%, 25%, and 15% compared to RAFT.  
In all experimental results, the proposed CAFT was superior to other methods by up to 77% and at least 3%. This result shows that the proposed method is superior to other methods in terms of fast optical flow calculation and accuracy while having a small number of network parameters.  
Conceptualization, methodology, and software, M.-H.P.; investigation, J.-H.C.; writing and original draft preparation, Y.-T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, and Forestry (IPET) through the Open Field Smart Agriculture Technology Short-Term Advancement Program, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (122032-03-1 SB010).  
Not applicable.  
FlyingChairs, FlyingThing3D: [[https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)]([https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)); Sintel: [[http://sintel.is.tue.mpg.de](http://sintel.is.tue.mpg.de)]([http://sintel.is.tue.mpg.de](http://sintel.is.tue.mpg.de)); KITTI: [[https://www.cvlibs.net/datasets/kitti/eval_flow.php](https://www.cvlibs.net/datasets/kitti/eval_flow.php)]([https://www.cvlibs.net/datasets/kitti/eval_flow.php](https://www.cvlibs.net/datasets/kitti/eval_flow.php)) (accessed on 26 November 2023).  
The authors declare no conflict of interest.