Real-Time Optical Flow Estimation Method Based on Cross-Stage Network

In this paper, a real-time optical flow estimation method based on a cross-stage network is proposed. The proposed model is designed with a network structure with encoders and decoders. The proposed method combines cross-stage network technology with the network structure of FlowNet2 and RAFT to ach...

Full description

Saved in:
Bibliographic Details
Published inApplied sciences Vol. 13; no. 24; p. 13056
Main Authors Park, Min-Hong, Cho, Jae-Hoon, Kim, Yong-Tae
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2023
Subjects
Accuracy
cross-stage
CSPNet
Deep learning
Design
Embedded systems
Estimates
FlowNet2
Neural networks
optical flow
Principles
RAFT
real-time
Teaching methods
Online AccessGet full text

Cover

More Information
Summary:In this paper, a real-time optical flow estimation method based on a cross-stage network is proposed. The proposed model is designed with a network structure with encoders and decoders. The proposed method combines cross-stage network technology with the network structure of FlowNet2 and RAFT to achieve improved parameter number and estimation performance. For real-time optical flow estimation, it is important to maintain performance while reducing the number of parameters in the network. In the proposed method, structural convergence is performed to increase performance while reducing the number of parameters by applying the cross-stage network structure. The proposed model is designed to solve the bottlenecks in model accuracy and complexity by separating feature extraction and flow estimation processes. Flying Chairs, Flying Things 3D, and KITTI datasets were used to evaluate the performance of the proposed model, and the experimental results show superior performance compared to previous traditional methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app132413056