3D Particle Filter Tracking
Adapting the Search Subspace of a Particle Filter using Geometric Constraints
In this work, we introduce a novel approach for object tracking using an adaptive Particle Filter operating on a point cloud based likelihood model. The novelty of the work comes from a geometric constraint detection and solving system which helps reduce the search subspace of the Particle Filter.
At every time step, it detects geometric shape constraints and associates it with the object being tracked. Using this information, it defines a new lower-dimensional search subspace for the state that lies in the nullspace of these constraints.
In this video, we demonstrate tracking using a limited number of particles with and without constraints applied.
The paper can be found here: Link