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Detecting Social Actions of Fruit Flies

Eyjolfsdottir, Eyrun and Branson, Steve and Burgos-Artizzu, Xavier P. and Hoopfer, Eric D. and Schor, Jonathan and Anderson­, David J. and Perona, Pietro (2014) Detecting Social Actions of Fruit Flies. In: Computer Vision – ECCV 2014. Lecture Notes in Computer Science. No.8690. Springer Internationa Publishing , Berlin, Germany, pp. 772-787. ISBN 978-3-319-10604-5.

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We describe a system that tracks pairs of fruit flies and automatically detects and classifies their actions. We compare experimentally the value of a frame-level feature representation with the more elaborate notion of 'bout features' that capture the structure within actions. Similarly, we compare a simple sliding window classifier architecture with a more sophisticated structured output architecture, and find that window based detectors outperform the much slower structured counterparts, and approach human performance. In addition we test our top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method. Our Fly-vs-Fly dataset contains 22 hours of video showing pairs of fruit flies engaging in 10 social interactions in three different contexts; it is fully annotated by experts, and published with articulated pose trajectory features.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Anderson­, David J.0000-0001-6175-3872
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2014 Springer. This work was supported by the ONR MURI N00014-10-l-0933 and the Gordon and Betty Moore Foundation.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-10-1-0933
Gordon and Betty Moore FoundationUNSPECIFIED
Series Name:Lecture Notes in Computer Science
Issue or Number:8690
Record Number:CaltechAUTHORS:20150113-085834106
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:53610
Deposited By: Ruth Sustaita
Deposited On:13 Jan 2015 20:01
Last Modified:09 Mar 2020 13:18

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