https://github.com/skp-1997/videostabilizationopencv
The code is primitive attempt to achieve video stabilization using OpenCV and without Deep Learning approach. It uses foundational computer vision techniques like feature detections, optical flow, transformation and warping.
https://github.com/skp-1997/videostabilizationopencv
computer-vision feature-detection image-transformations opencv optical-flow video-stabilization
Last synced: 8 months ago
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The code is primitive attempt to achieve video stabilization using OpenCV and without Deep Learning approach. It uses foundational computer vision techniques like feature detections, optical flow, transformation and warping.
- Host: GitHub
- URL: https://github.com/skp-1997/videostabilizationopencv
- Owner: skp-1997
- Created: 2024-02-18T01:11:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-18T20:32:20.000Z (over 1 year ago)
- Last Synced: 2025-01-02T14:34:42.664Z (9 months ago)
- Topics: computer-vision, feature-detection, image-transformations, opencv, optical-flow, video-stabilization
- Language: Python
- Homepage:
- Size: 27.3 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# Video Stabilization using Optical Flow
This code is simple attempt to stabilize (smoothen) a video using just traditional computer vision techniques using OpenCV. It's not best but will surely help learn many small concepts in computer vision like feature detection, optical flow, transformation and warping.
# Demonstration:
https://github.com/skp-1997/videoStabilizationOpenCV/assets/97504177/7abd54a9-cde6-4c06-968b-88dc28d48825
# Steps for the Video Stabilization
## [1] Detectng features from the frame
Here, I am using 'goodFeaturesToTrack' from OpenCV to detect feature points
## [2] Calculating Optical Flow
I am using 'calcOpticalFlowPyrLK' from OpenCV to calculate optical flow in concurrent frames from features ppints detected in previous frame. It uses Lucas-Kanade Pyramid method to calculate the pixel positions.
## [3] Estimate motion b/w two frames
With the help of 'estimateRigidTransform' module, I calculated the transformation values [x, y, theta] b/w frames.
To get the idea of how it smoothen the curve, here is the picturization.
## [4] Calculate the smooth motion for entire video
First, I use 'numpy.cumsum' to get trajectory for entire video, which later was used to smoothen the transformation using filtering.
I am using 'MovingAverageFilter', the logic is defined below.
The filter is applied trajectory matrix which smoothen values along translation along x, y and rotation along x direction.
## [5] Warping using smoothen transformation matrix calculated before.
Using 'cv2.warpAffine' to wrap consecutive frames from the filtered trajectory matrix.
## [6] Fixing the borders
Since we are warping image, to maintain the frame size. This will led to some dead pixels along border which will be visible in the output video.
# References:
1. Video Stabilization Using Point Feature Matching in OpenCV - Abhishek Singh Thakur
https://learnopencv.com/video-stabilization-using-point-feature-matching-in-opencv/
2. Optical Flow in OpenCV (C++/Python) - Maxim Kuklin (Xperience.AI)
https://learnopencv.com/optical-flow-in-opencv/
3. CS231M · Mobile Computer Vision - Standford University
https://web.stanford.edu/class/cs231m/lectures/lecture-7-optical-flow.pdf
# Scope of Improvement
The method is primitive and doesn't work if there are objects moving in video at faster pace. The other approached would be to find where optical flow is maximum and compensate for that using mathematical logic or use deep learning model.