Particle filtering with rendered models: A two pass approach to multi-object 3D tracking with the GPU

Abstract

We describe a new approach to vision-based 3D object tracking, using appearance-based particle filters to follow 3D model reconstructions. This method is targeted towards modern graphics processors, which are optimized for 3D reconstruction and are capable of highly parallel computation. We discuss an OpenGL implementation of this approach, which uses two rendering passes to update the particle filter weights. In the first pass, the system renders the previous object state estimates to an off-screen framebuffer. In the second pass, the system uses a programmable vertex shader to compute the mean normalized cross-correlation between each sample and the subsequent video frame. The particle filters are updated using the correlation scores and provide a full 3D track of the objects. We provide examples for tracking human heads in both single and multi-camera scenarios.