In contrast to video sensors which just “watch” the world, present-day research is aimed at developing intelligent devices able to interpret it locally. A number of such devices are available on the market, very powerful on the one hand, but requiring either connection to the power grid, or massive rechargeable batteries on the other. MicrelEye, the wireless video sensor node presented in this paper, targets a different design point: portability and a scanty power budget, while still providing a prominent level of intelligence, namely objects classification. To deal with such a challenging task, we propose and implement a new SVMlike hardware-oriented algorithm called ERSVM. The case study considered in this work is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing local classification tasks.

Distributed Video Surveillance Using Hardware-Friendly Sparse Large Margin Classifiers / A. Kerhet; F. Leonardi; A. Boni; P. Lombardo; M. Magno; L. Benini. - STAMPA. - (2007), pp. 87-92. (Intervento presentato al convegno Advanced Video and Signal based Surveillance tenutosi a London, UK nel September 5 - 7, 2007).

Distributed Video Surveillance Using Hardware-Friendly Sparse Large Margin Classifiers

MAGNO, MICHELE;BENINI, LUCA
2007

Abstract

In contrast to video sensors which just “watch” the world, present-day research is aimed at developing intelligent devices able to interpret it locally. A number of such devices are available on the market, very powerful on the one hand, but requiring either connection to the power grid, or massive rechargeable batteries on the other. MicrelEye, the wireless video sensor node presented in this paper, targets a different design point: portability and a scanty power budget, while still providing a prominent level of intelligence, namely objects classification. To deal with such a challenging task, we propose and implement a new SVMlike hardware-oriented algorithm called ERSVM. The case study considered in this work is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing local classification tasks.
2007
Proceedings of IEEE Conference on Advanced Video and Signal based Surveillance, 2007 (AVSS 2007)
87
92
Distributed Video Surveillance Using Hardware-Friendly Sparse Large Margin Classifiers / A. Kerhet; F. Leonardi; A. Boni; P. Lombardo; M. Magno; L. Benini. - STAMPA. - (2007), pp. 87-92. (Intervento presentato al convegno Advanced Video and Signal based Surveillance tenutosi a London, UK nel September 5 - 7, 2007).
A. Kerhet; F. Leonardi; A. Boni; P. Lombardo; M. Magno; L. Benini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/49841
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