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Optical Flow 추정을 위한 딥러닝 기반의 Lightweight Deep Neural Network
최주성(Joo sung Choi),김중희(Jung Hee Kim),박재서(Jae Seo Park),강석주(Suk-Ju Kang) 대한전자공학회 2019 대한전자공학회 학술대회 Vol.2019 No.11
Conventional methods for optical flow estimation are inappropriate for applications that demand real-time operation and small memory capacity. Deep learning based networks, such as FlowNet[l] and FlowNet2[2], have high accuracy and fast running time, but require more than 160M of parameters. Therefore, we introduce tow deep learning based networks called PWC-Net[3] and LiteFlowNet[4] that have compact number of parameters and also have effective performances. Introducing two network models of compact but effective CNN models for optical flows, called PWC-Net and LiteFlowNet.