Book_Computational_Intelligence_From_Theory_to_Application_explores_augmented_optical_flow_methods_for_video_stabilization

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PDF Download book chapter titled “Augmented Optical Flow Methods for Video Stabilization”, In Computational Intelligence: from theory to application. (2017) (p18).

Computational Intelligence: From Theory to Application

Chapter 4: Augmented Optical Flow Methods for Video Stabilization

1. Introduction

• Purpose of Video Stabilization: Eliminates undesired motion from camera frames.
  • Important for mobile robot vision systems.
  • Motion analysis is a major task in computer vision.
  • Interpretation of motion is crucial in dynamic scenes.
• Applications: Computer vision tasks in diverse fields (e.g., mobile robots).
  • Reduces blurring due to camera motion.
  • Key qualitative feature for vision systems.
  • Importance of motion analysis for camera and scene interactions.

2. Related Work

• Video Stabilization (VS) Techniques:
  • Applied to reduce jitter and motion blur.
  • Optical flow methods: Incorporate motion features for stabilization.
• Dense 2D Motion Field:
  • Optical flow-based video stabilization model.
  • Combines feature point selection and delta optical flow.
• Combining Optical Flow and Inertial Measurement:
  • Enhances stabilization by integrating optical flow with additional sensors.
• Methods:
  • Farneback’s method (2003): Polynomial expansion for motion estimation.
  • SimpleFlow (Tao et al., 2012): Fast optical flow method.
  • Deepflow (Weinzaepfel et al., 2013): Optical flow with large displacement matching.

3. The Proposed Method

• Enhanced Optical Flow Using Type 2 Fuzzy Gaussian Pyramid (T2FGPOF):
  • Gaussian pyramid levels for image resizing.
  • Improves optical flow accuracy by using optimized Gaussian settings.
• Type 2 Fuzzy Logic System (T2FLS):
  • T2FLS divides fuzzy systems into general and interval types.
• Membership Function Inputs:
  • NIS: Normalized Image Size.
  • SM: Sample Motion.
  • IQAF: Image Quality Assessment Function.
• Gaussian Pyramid Optimization:
  • Parameters: Number of levels, downscale factor, standard deviation.
  • Gaussian pyramid provides multi-scale image representation.

4. Optical Flow Estimation

• Pyramid Representation:
  • Multi-scale image smoothing and sub-sampling (pyramid representation).
  • Applied in texture synthesis and optical flow.
• General Fuzzy Logic (T2FLS) Approach:
  • Inputs include normalized image size, motion samples, and sharpness function.
  • Type-2 FLS output sets Gaussian pyramid parameters for stabilization.
• Type 2 Fuzzy Sets:
  • Footprint of uncertainty (FOU) defines the bounds of fuzzy membership functions.

5. Video Stabilization Phases

• Preliminary Processing (PP):
  • Normalized image size, sample motion, and image quality assessment.
  • Gaussian pyramid parameter settings calculated in this phase.
• Optical Flow Algorithm:
  • Uses pyramid parameters to enhance motion estimation.
  • Applies Gaussian pyramid to calculate optical flow for stabilization.

6. Experimental Setup and Results

• Dataset:
  • MPI SINTEL benchmark dataset.
  • Test cases: Alley, Ambush, Bamboo, and Cave.
• Comparison with Farneback’s Method:
  • Improvements in large motion, motion blur, and defocus blur.
  • Reduced errors in angular errors (measured in pixels).
  • Specific improvements:
    • Alley: Reduced error from 2.033 to 0.721 pixels.
    • Ambush: Minor improvement in defocus blur.
    • Bamboo: Major reduction in motion blur from 2.575 to 0.884 pixels.
    • Cave: Significant improvement in large motion reduction.

7. Detailed Algorithm Components

• Fuzzifier: Inputs NIS, SM, and IQAF into fuzzy system.
• Rule Base and Inference Engine: Manages the fuzzy rules and derives outputs.
• Defuzzifier: Converts fuzzy outputs to crisp values (Gaussian pyramid parameters).
• Gaussian Pyramid Construction:
  • Pyramid built using levels, downscale factor, and smoothing parameters.
  • Pyramid used to estimate optical flow for motion stabilization.

8. Comparison of Video Stabilization Techniques

• Traditional Methods:
  • Farneback’s method: Relies on polynomial expansion for optical flow.
  • SimpleFlow: Fast but lacks robustness for complex motion.
  • DeepFlow: Handles large displacements but computationally intensive.
• Proposed Method (T2FGPOF):
  • Incorporates fuzzy logic for more adaptive parameter settings.
  • Outperforms traditional methods in handling complex motion scenarios.

9. Conclusion

• Effectiveness of T2FGPOF:
  • Enhances video stabilization through improved optical flow estimation.
  • Adapts parameters based on fuzzy logic for more accurate motion compensation.
• Impact:
  • Reduction of motion blur and defocus issues in challenging video sequences.
  • Significant performance improvements over existing techniques.

10. References

• References to various methods and studies on optical flow and video stabilization.
  • Notable works by Farneback (2003), Liu et al. (2014), Wagner & Hagras (2010).
  • Contributions from research on motion estimation, optical flow algorithms, and fuzzy logic systems.