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.

Enabling visual analysis in wireless sensor networks

BAROFFIO, LUCA;CANCLINI, ANTONIO;CESANA, MATTEO;REDONDI, ALESSANDRO ENRICO CESARE;TAGLIASACCHI, MARCO;
2014

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.
Image Processing (ICIP), 2014 IEEE International Conference on
Linux; Zigbee; cameras; data compression; feature extraction; image coding; object recognition; object tracking; wireless sensor networks; ATC paradigm; BeagleBones Linux computers; CTA paradigm; GreenEyes project; IEEE 802.15.4 communication capabilities; JPEG compressed image transmission; analyze-then-compress paradigm; application frame rates; bandwidth constrained scenario; camera node; central controller; compress-then-analyze paradigm; distributed image acquition; local binary visual feature compression; local binary visual feature extraction; local image acquition; multihop visual sensor network; resource-constrained multimedia sensor networks; visual analysis; visual paradigms; Transform coding; Visualization; ARM; Binary Local Visual Features; Visual Sensor Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/945564
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