Parametrization of acoustic images for the detection of human presence by mobile platforms

We address the problem of human detection with mobile platforms such as robots. Instead of using an optical system, we propose to employ an acoustic 2D array to reliably obtain an image of a human in a 3D spatial power spectrum which is independent of lighting conditions and uses cheap acoustic sensors. We show that humans have a distinct acoustic signature and propose to model the echoes from reflecting parts of objects in the scene by a Gaussian-Mixture-Model. When it is fitted to the acoustic image, we can extract geometric relations between the present echoes and represent the acoustic signatures in a low-dimensional parameter space. We present results based on real data measurements that demonstrate that different objects can be reconstructed from the data and discriminated. The obtained parameter space forms the basis for subsequent detection and classification of humans.