A Kalman filter based visual tracking algorithm for an object moving in 3D

Robust and effective real-time visual tracking is realized by combining the first order differential invariants with stochastic filtering. The Kalman filter as an optimal stochastic filter is used to estimate the motion parameters, namely the plant state vector of the moving object with the unknown dynamics in successive image frames. Using the fact that the relative motion between the moving object and the moving observer causes the deformation, we compute the first differential invariants of the image velocity field. The surface orientation and the depth estimate between the observer and the object are computed based on these first order differential invariants. We demonstrate the robustness and feasibility of the proposed tracking algorithm through real experiments in which an X-Y Cartesian robot tracks a toy vehicle moving along 3D rails.

Authors

• Joon Woong Lee
• Mun Sang Kim
• In-So Kweon

Keywords

• Robustness
• Stochastic processes
• Filtering
• Filters
• Motion estimation
• Parameter estimation
• State estimation
• Vehicle dynamics
• Observers
• Robots
• Kalman Filter
• Tracking Algorithm
• Image Frames
• Image Distortion
• Depth Estimation
• Surface Orientation
• Unknown Dynamics
• Transition State
• Center Of Mass
• Measurement Model
• Angular Velocity
• Optical Axis
• Position Vector
• Feature Points
• Linear Velocity
• Error Covariance
• Target Velocity
• System State Vector
• Pure Shear
• Surface Normal Vector
• Camera Coordinate System
• Monocular Images
• Triangular Grid