Optical Flow Estimation
650 papers with code • 10 benchmarks • 33 datasets
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
Benchmarks
These leaderboards are used to track progress in Optical Flow Estimation
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Libraries
Use these libraries to find Optical Flow Estimation models and implementationsDatasets
KITTI
FlyingThings3D
Vimeo90K
MPI Sintel
Virtual KITTI
MegaDepth
VisDrone
Event-Camera Dataset
N-CARS
MVSEC
Latest papers
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation
hmorimitsu/ptlflow
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IEEE International Conference on Robotics and Automation (ICRA) 2024
Extracting motion information from videos with optical flow estimation is vital in multiple practical robot applications.
Moving Object Segmentation: All You Need Is SAM (and Flow)
The objective of this paper is motion segmentation -- discovering and segmenting the moving objects in a video.
DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping
Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy.
Rethinking Low-quality Optical Flow in Unsupervised Surgical Instrument Segmentation
wpr1018001/rethinking-low-quality-optical-flow
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Video-based surgical instrument segmentation plays an important role in robot-assisted surgeries.
LSTP: Language-guided Spatial-Temporal Prompt Learning for Long-form Video-Text Understanding
Despite progress in video-language modeling, the computational challenge of interpreting long-form videos in response to task-specific linguistic queries persists, largely due to the complexity of high-dimensional video data and the misalignment between language and visual cues over space and time.
CREMA: Multimodal Compositional Video Reasoning via Efficient Modular Adaptation and Fusion
Yui010206/CREMA
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Furthermore, we propose a fusion module designed to compress multimodal queries, maintaining computational efficiency in the LLM while combining additional modalities.
Motion-Aware Video Frame Interpolation
zdyshine/Video-Frame-Interpolation-Summary
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Subsequently, a cross-scale motion structure is presented to estimate and refine intermediate flow maps by the extracted features.
Taylor Videos for Action Recognition
Addressing these challenges, we propose the Taylor video, a new video format that highlights the dominate motions (e. g., a waving hand) in each of its frames named the Taylor frame.
Recurrent Partial Kernel Network for Efficient Optical Flow Estimation
hmorimitsu/ptlflow
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The 38th Annual AAAI Conference on Artificial Intelligence (AAAI) 2024
However, this impacts the widespread adoption of optical flow methods and makes it harder to train more general models since the optical flow data is hard to obtain.
