A diffusion framework for detection of moving vehicles

We introduce a novel real-time algorithm for automatic acoustic-based vehicle detection. Commonly, surveillance systems for this task use a microphone that is placed in a target area. The recorded sounds are processed in order to detect vehicles as they pass by. The proposed algorithm uses the wavelet-packet transform in order to extract spatio-temporal characteristic features from the recordings. These features constitute a unique acoustic signature for each of the recordings. A more compact signature is derived by the application of the Diffusion Maps (DM) dimensionality reduction algorithm. A new recording is classified according to its compact acoustic signature in the DM reduced-dimension space. The signature is efficiently obtained via the Geometric Harmonics (GH) algorithm. The introduced algorithm is generic and can be applied to various signal types for solving different detection and classification problems.