Robust Video Stabilizer: Outdoor Scene

Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as required in many practical applications. In this system, we propose a novel technique for video stabilization based on the particle filtering framework. Scale-invariant feature points are extracted to form a rough estimate which is used to model the importance density. We use a constant-velocity Kalman filter model to estimate intentional camera movement. We also prove that the particle filtering estimate will lower the error variance. The superior performance and robustness of our algorithm is demonstrated by computer simulations [Reference1: Junlan Yang, Dan Schonfeld, Chong Chen, and Magdi Mohamed, "Online Video Stabilization Based on Particle Filters", in the IEEE International Conference on Image Processing (ICIP'06), Atlanta, Georgia, October 8-11, 2006], [Reference2: Junlan Yang, Dan Schonfeld, and Magdi Mohamed, Robust Video Stabilization Based on Particle Filtering of Projected Camera Motion, in the IEEE Transactions on Circuits and Systems for Video Technologies, Volume 19, Issue 7, pp. 945-954, July 2009].

Category:

Science & Technology

Tags:

• Robust-Video-Stabilizer
• Image-Motion-Analysis
• Image-Sequence-Analysis
• Particle-Filter
• Monte-Carlo-Methods