900260 Automated Visual Event Detection and Classification : AVEDac Introduction This page last changed on Mar 30, 2010 by dcline. ## Overview of the AVEDac project In order to study the distribution and abundance of oceanic animals, MBARI and other oceanographic institutes, use high-resolution video equipment on remotely operated vehicles. Quantitative video transects (QVTs) supplant traditional net tows to assess the quantity and diversity of organisms in the water column. QVTs are run from 50 m to 4000 m and provide high-resolution data at the scale of the individual animals as well as their natural aggregation patterns. However, the current, manual method of analyzing QVTs is labor intensive and tedious. AVEDac is a software solution designed for automating the detection of animals in underwater video to enhance the productivity of human video annotators. It was developed by the Monterey Bay Aquarium Research Institute in collaboration with the University of Southern California and the California Institute of Technology. AVED is based on the USC iLab Neormorphic Visual C++ Toolkit and is designed to detect, track, and classify animals in underwater. The continued use of ROVs and future use of Autonomous Underwater Vehicles (AUVs) for QVTs offer potential for even more data, perhaps many times what we current collect and analyze. Hence, we see tremendous benefit in automating portions of the analysis. We also see great benefit in automating analysis of video from fixed ocean observatory cameras, where autonomous response to potential events (pan/zoom to events), and automated processing of largely "boring" (event sparse) video streams from 10s or 100s or even 1000s of network cameras could be key to those cameras being useful practical scientific instruments. ### User Support See the AVEDac Support for help.

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