Detecting Moving Objects Using a Camera on a Moving Platform
Chung-Ching Lin, Marilyn C. Wolf · 2010
This paper proposes a new ego-motion estimation and background/foreground classification method to effectively segment moving objects from videos captured by a moving camera on a moving platform. Existing methods for moving-camera detecting impose serious constraints. In our approach, ellipsoid scene shape is applied in the motion model and a complicated ego-motion estimation formula is derived. Genetic algorithm is introduced to accurately solve ego-motion parameters. After motion recovery, noisy result is refined by motion vector correlation and foreground is classified by pixel level probability model. Experiment results show that the method demonstrates significant detecting performance without further restrictions and performs effectively in complex detecting environment.