Average End-Point Error: 1.092
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Optical Flow Estimation
Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
10개논문
Average End-Point Error: 1.519
Average End-Point Error: 1.046
Average End-Point Error: 1.16
Average End-Point Error: 1.390
Average End-Point Error: 1.388
Average End-Point Error: 0.963
Average End-Point Error: 1.81
Average End-Point Error: 1.609
Average End-Point Error: 1.315
Average End-Point Error: 1.27
Average End-Point Error: 1.03
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