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An accumulative fusion architecture for discriminating people and vehicles using acoustic and seismic signals

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
KustantajaIEEE
Sivut2976-2980
Sivumäärä5
ISBN (elektroninen)9781509041176
DOI - pysyväislinkit
TilaJulkaistu - 16 kesäkuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING -
Kesto: 1 tammikuuta 19001 tammikuuta 2000

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING
Ajanjakso1/01/001/01/00

Tiivistelmä

In this paper, we develop new multiclass classification algorithms for detecting people and vehicles by fusing data from a multimodal, unattended ground sensor node. The specific types of sensors that we apply in this work are acoustic and seismic sensors. We investigate two alternative approaches to multiclass classification in this context - the first is based on applying Dempster-Shafer Theory to perform score-level fusion, and the second involves the accumulation of local similarity evidences derived from a feature-level fusion model that combines both modalities. We experiment with the proposed algorithms using different datasets obtained from acoustic and seismic sensors in various outdoor environments, and evaluate the performance of the two algorithms in terms of receiver operating characteristic and classification accuracy. Our results demonstrate overall superiority of the proposed new feature-level fusion approach for multiclass discrimination among people, vehicles and noise.

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