Enhancing the video quality to exclude unwanted jitter and camera shakes by using efficient techniques is called video stabilization. The main reasons behind these quality degrading factors are unintended camera panning and hand jiggling. This page mainly discusses a complete overview of Matlab Video Stabilization projects which address both current and future research directions!!!
What does video stabilization do?
To reduce visual quality loss, video stabilization technology is introduced. In this, it eliminates undesirable jitters and shakes of cameras/scanners without affecting camera panning and mobile objects. Mainly, it is used in both offline systems and real-time systems.
In specific, some of the handheld imaging devices are not influenced by utilizing shakes. By the by, the unbalanced images series are naturally created through intentional camera panning, camera position variation, unwanted hand jiggling, etc.
Currently, stable video series and visual quality are effectively achieved in advanced video stabilization techniques. The mechanical imaging devices can ignore physical camera shakes.
Importance of Video Stabilization
To recognize a specific portion of the image like a number plate, human face, object, etc., image quality is significant particularly in various video surveillance cameras. In that case, it integrates different video frames. Therefore, video stabilization is required to implement with simple computation. This process enhances the image quality in real-time video sequences.
The main objective of video stabilization is to efficiently generate a new video sequence. By the by, it accurately eliminates the video motion among frames in an effective manner. In specific, it estimates continuous irregular motion by computing inter-frame motion. Next, we can see in what way, motions between frames are detected in Matlab Video Stabilization.
How does motion detect video stabilization?
- Step 1 – Collect the original video sequence
- Step 2 – Implement image pre-processing by image resizing and image filtering
- Step 3 – Implement SURF algorithm and nearest neighbors matching algorithm
- Step 4 – Apply improved RANSAC method to eliminate error match points
- Step 5 – Perform parameter cascading method
- Step 6 – Use Kalman filter through movement smoothing
- Step 7 – Apply for motion compensation
- Step 8 – Stabilize video sequence by correcting frames
Now, we can see the general development steps to execute matlab code for video stabilization projects. By the by, these steps may vary further based on your project requirements. When you connect with us, we provide you implementation plan for your selected video stabilization project using Matlab.
Further, it includes system requirements (fundamental hardware and software) along with dataset and performance evaluation metrics. So, choose the best project from our list to know the execution steps for your proposed research works.
How does matlab video stabilization works
- Step 1 – Collect the frame sequence from a Movie File (i.e., video)
- Step 2 – Identify and acquire important points in every frame
- Step 3 – Choose correspondences among different points
- Step 4 – Based on noisy correspondence, compute the transform
- Step 5 – Perform smoothing and transform approximation
- Step 6 – Generate and Execute the full corrected video
For illustration purposes, here we have given you important procedures to eliminate camera motion implication while streaming video. The main objective is to focus on camera movement to enhance video stabilization and reduce computation in the target area. Majorly, it involves two phases such as initialization and stream processing loop. Follow our steps in these phases to achieve our project objectives.
Initially, define the target to be identified in the video. Then set the dynamic search area where the position is estimated based on the last known location of the target. Minimize the computation to identify the target in the search area. In sequential video frames, compute the number of targets moved concerning the previous frame. Then, it is used to eliminate the impact of undesired camera movements. After eliminating such motions, generate a stabilized video in high-quality.
- Read video from multimedia file to make system object We set the output to be of intensity only video.
- To find the target location in the video frame, make a template matcher by system object
- Utilize this location to estimate translation among sequential frames of video
- Develop system object to show stabilized and original video
- Initiate the variables employed in the processing loop
Stream Processing Loop
In this phase, one can stabilize the input video by using the above-initiated system objects. Then, fulfil the below requirements to complete this video stabilization process.
- To identify target location input video frame
- To render video frame to offset the movement of the camera
- To add a black border for display
- To draw rectangles on input to display search area and target
- To visualize offset values on the input image
- To visualize video for display
Next, we can see the important video stabilization toolboxes associated with MATLAB software. Generally, MATLAB is sophisticated with a tremendous number of libraries, modules, and toolboxes. All these are collectively used to perform any kind of complex operations on image and video processing. Since each one is specialized to solve certain research problems of video/image processing. Overall, this software is motivated to achieve enhance the performance of multimedia information processing. Here, we have given you the main toolboxes that are effective to handle video stabilization processes.
Matlab Toolbox for Video Stabilization
- Develop complex systems like video stabilization by using the MATLAB tool
- Comprised with Constrained-3D Rotation Smoothing Toolbox
- Intended to implement smoothing operation over 3D rotation matrices sequences
- In this, smoothing is treated as a constrained regression issue which is solved using the 2-metric projection technique
- Then, follow below three steps for Matlab video stabilization
- Step 1 – Compute the camera motion
- Step 2 – Perform smoothing over motion
- Step 3 – Execute image warping
- In each frame, a 3D rotation matrix is determined by gyro readings for camera motion computation (3D Rotation Smoothing Toolbox is used for step 2)
- Implement required functions from Computer Vision Toolbox
Further, we have also given you some important research techniques that are widely used to develop video stabilization. In this, we have specified two main metrics such as speed and accuracy for each technique. Every technique has some special characteristics to sort out specific operations.
All these techniques are extensively recognized in numerous MATLAB video stabilization projects. Moreover, we also developed other important research solutions like algorithms and techniques for different video stabilization projects.
Methods for Video Stabilization
- Gaussian Mixture
- Speed – Acceptable Speed
- Accuracy – Noise Reduction and Adaptive Lighting Variation
- Gaussian Average
- Speed – Rapid Execution because of Simple Computation
- Accuracy – Low Performance and False Alert / Noise Detection
- Eigen Backgrounds
- Speed – Based on the number of Eigen Vectors
- Accuracy – Train set for achieving a good outcome
- Kernel Density
- Speed – Require Add-on algorithm for Real-world Execution
- Accuracy – Fault-tolerant and Efficient
Now, we can see about performance metrics that are used to evaluate the video stabilization projects. Most importantly, these metrics are used to assess the efficiency of the prosed systems. Also, these metrics will differ from your handpicked video stabilization project requirements.
In our development plan, we provide you list of performance evaluation metrics before implementation. All our suggested metrics are sure to deliver the best results by enhancing the efficiency of proposed research solutions. Further, there are a massive amount of performance assessment metrics for Matlab video stabilization projects.
Video Stabilization Performance Metrics
- Structural Similarity Index
- Inter-frame Similarity Index
- Average Speed
- Average Acceleration
- Inter-frame Transformation Fidelity
- Average Percentage of Conserved Pixels
Last but not least, now we can see about future research directions of video stabilization. For any project topic, it is necessary to look for future scope for future studies. When you are selecting a project topic without the future scope, it is not worth doing research. So, make sure that you have the highest degree of future scope for your selected project topic in the video stabilization field.
Here, we have listed only a few important project topics that have an impact on future technologies. If you are curious to know other research directions then approach us. Based on your interest, we suggest you a list of project ideas for video stabilization using the Matlab tool.
Future Research Directions of Video Stabilization
- Real-time Video Stabilization in Outdoor Scene (Holding portable camera while walking on staircases)
- Hybrid-camera System over Checked Pattern needs to be Verified
- Photographic Pattern-based Video Stabilization by removing Low-velocity and Jitter
To the end, we are ready to share our latest collection of video stabilization and video processing research ideas. We have separate researchers’ teams, developers’ teams, and writers’ teams to support you from every research stage of video stabilization. All our team members are adept to work with advanced technologies to produce the finest research results. Once make a bond with us, we allocate you nearly 10+ experts of these teams.
They will take whole responsibilities to guide you in matlab video stabilization research code execution and manuscript writing. Overall, our handhold scholars and final year students to avail themselves all these services in one place. So, contact our team to achieve your research goal in the field of video stabilization.