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- Baroffio, L., et al.
(författare)
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Demo : Enabling image analysis tasks in visual sensor networks
- 2014
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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-
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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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| 2. |
- Baroffio, L., et al.
(författare)
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GreenEyes : Networked energy-aware visual analysis
- 2015
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Ingår i: 2015 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2015. - : IEEE conference proceedings. - 9781479970797
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Konferensbidrag (refereegranskat)abstract
- The GreenEyes project aims at developing a comprehensive set of new methodologies, practical algorithms and protocols, to empower wireless sensor networks with vision capabilities. The key tenet of this research is that most visual analysis tasks can be carried out based on a succinct representation of the image, which entails both global and local features, while it disregards the underlying pixel-level representation. Specifically, GreenEyes will pursue the following goals: i) energy-constrained extraction of visual features; ii) rate-efficiency modelling and coding of visual feature; iii) networking streams of visual features. This will have a significant impact on several scenarios including, e.g., smart cities and environmental monitoring.
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