Dimensionality reduction for detection of moving vehicles

A. Averbuch*, N. Rabin, A. Schclar, V. Zheludev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Automatic acoustic-based vehicle detection is a common task in security and surveillance systems. Usually, a recording device is placed in a designated area and a hardware/software system processes the sounds that are intercepted by this recording device to identify vehicles only as they pass by. An algorithm, which is suitable for online automatic detection of vehicles, which is based on their online acoustic recordings, is proposed. The scheme uses dimensionality reduction methodologies such as random projections instead of using traditional signal processing methods to extract features. It uncovers characteristic features of the recorded sounds without any assumptions about the structure of the signal. The set of features is classified by the application of PCA. The microphone is opened all the time and the algorithm filtered out many background noises such as wind, steps, speech, airplanes, etc. The introduced algorithm is generic and can be applied to various signal types for solving different detection and classification problems.

Original languageEnglish
Pages (from-to)19-27
Number of pages9
JournalPattern Analysis and Applications
Issue number1
StatePublished - Feb 2012


FundersFunder number
Ministry of Science and Technology, Israel


    • Detection of moving vehicles
    • Diffusion maps
    • Dimensionality reduction
    • PCA


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