NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots
  Using Edge Devices
Real-time high-accuracy optical flow estimation is a crucial component in
various applications, including localization and mapping in robotics, object
tracking, and activity recognition in computer vision. While recent
learning-based optical flow methods have achieved high accuracy, they often
come with heavy computation costs. In this paper, we propose a highly efficient
optical flow architecture, called NeuFlow, that addresses both high accuracy
and computational cost concerns. The architecture follows a global-to-local
scheme. 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. We evaluate our
approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency
improvements across different computing platforms. We achieve a notable 10x-80x
speedup compared to several state-of-the-art methods, while maintaining
comparable accuracy. Our approach achieves around 30 FPS on edge computing
platforms, which represents a significant breakthrough in deploying complex
computer vision tasks such as SLAM on small robots like drones. The full
training and evaluation code is available at
https://github.com/neufieldrobotics/NeuFlow.
65f8196099c842dd939da849
Zhiyong Zhang
extracted_pending
2024-04-17T03:47:42.122Z
661f4653702ad39754d94ac0
/avatars/82902d642a603ce04cbcd3507661de32.svg
Zhiyong Zhang
user
Study-is-happy
65f8196099c842dd939da84a
Huaizu Jiang
65f8196099c842dd939da84b
Hanumant Singh
admin_assigned
2024-03-18T12:29:15.010Z
624f308f915512ce8b3fccee
/avatars/0301164b6fd6ad4247cfad62fd859367.svg
Hanumant Singh
user
hanumant
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots
  Using Edge Devices
Real-time high-accuracy optical flow estimation is a crucial component in
various applications, including localization and mapping in robotics, object
tracking, and activity recognition in computer vision. While recent
learning-based optical flow methods have achieved high accuracy, they often
come with heavy computation costs. In this paper, we propose a highly efficient
optical flow architecture, called NeuFlow, that addresses both high accuracy
and computational cost concerns. The architecture follows a global-to-local
scheme. 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. We evaluate our
approach on Jetson Orin Nano and RTX 2080 to demonstrate efficiency
improvements across different computing platforms. We achieve a notable 10x-80x
speedup compared to several state-of-the-art methods, while maintaining
comparable accuracy. Our approach achieves around 30 FPS on edge computing
platforms, which represents a significant breakthrough in deploying complex
computer vision tasks such as SLAM on small robots like drones. The full
training and evaluation code is available at
https://github.com/neufieldrobotics/NeuFlow.
NeuFlow, a global-to-local optical flow architecture, delivers high accuracy with significant speed improvements across various computing platforms, enabling real-time deployment on edge devices.
optical flow estimation computer vision localization mapping object tracking activity recognition global matching lightweight CNN layers speedup