Analysis of Acoustic Signatures from Moving Vehicles UsingTime-Varying Autoregressive Models
Multidimensional Systems and Signal Processing
A Wavelet Packet Algorithm for Classification and Detectionof Moving Vehicles
Multidimensional Systems and Signal Processing
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based acoustic detection of moving vehicles
Multidimensional Systems and Signal Processing
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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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.