Enabling visual analysis in wireless sensor networks

• Main Authors: Baroffio, L., Canclini, A., Cesana A. Redondi, M., Tagliasacchi, M., Dan, G., Eriksson, E., Fodor, V., Ascenso, J., Monteiro, P.
• Format: Conference Proceeding
• Language: English
• Subjects: ARM; Binary Local Visual Features; Cameras; Feature extraction; Image coding; Object recognition; Object Tracking; Transform coding; Visual Sensor Networks; Visualization; Wireless sensor networks
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2014.7025690

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.