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Detecting Actions of Fruit Flies


Eyjolfsdottir, Eyrun Arna (2014) Detecting Actions of Fruit Flies. Master's thesis, California Institute of Technology. doi:10.7907/SD8H-YB17.


In this thesis we describe a system that tracks fruit flies in video and automatically detects and classifies their actions. We introduce Caltech Fly-vs-Fly Interactions, a new dataset that contains hours of video showing pairs of fruit flies engaging in social interactions, and is published with complete expert annotations and articulated pose trajectory features. 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 the top performing detector on the CRIM13 mouse dataset, finding that it matches the performance of the best published method.

Item Type:Thesis (Master's thesis)
Subject Keywords:action detection, action classification, fruit flies
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Computer Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Perona, Pietro
Thesis Committee:
  • None, None
Defense Date:April 2014
Non-Caltech Author Email:eyrun.eyjolfsdottir (AT)
Funding AgencyGrant Number
Leifur Eiriksson FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechTHESIS:04212014-143100101
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8195
Deposited By: Eyrun Eyjolfsdottir
Deposited On:28 Apr 2014 21:57
Last Modified:08 Nov 2023 00:44

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