SwePub - sökning: WFRF:(Tagliasacchi M.)

Numrering	Referens	Omslagsbild	Hitta
1.	Baroffio, L., et al. (författare) Demo : Enabling image analysis tasks in visual sensor networks 2014 Ingår i: Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450329255 ; , s. a46- Konferensbidrag (refereegranskat)abstract This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.

Baroffio, L., et al. (författare)
Demo : Enabling image analysis tasks in visual sensor networks
2014
Ingår i: Proceedings of the 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450329255 ; , s. a46-
Konferensbidrag (refereegranskat)abstract
- This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.

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