title: Automated tracking and analysis of behavior in restrained insects.
abstract: BACKGROUND
  Insect behavior is often monitored by human observers and measured in the form of binary responses. This procedure is time costly and does not allow a fine graded measurement of behavioral performance in individual animals. To overcome this limitation, we have developed a computer vision system which allows the automated tracking of body parts of restrained insects.
  
  
  METHODS
  Our system crops a continuous video into separate shots with a static background. It then segments out the insect's head and preprocesses the detected moving objects to exclude detection errors. A Bayesian-based algorithm is proposed to identify the trajectory of each body part.
  
  
  RESULTS
  We demonstrate the application of this novel tracking algorithm by monitoring movements of the mouthparts and antennae of honey bees and ants, and demonstrate its suitability for analyzing the behavioral performance of individual bees using a common associative learning paradigm.
  
  
  METHODS
  Our tracking system differs from existing systems in that it does not require each video to be labeled manually and is capable of tracking insects' body parts even when working with low frame-rate videos. Our system can be generalized for other insect tracking applications.
  
  
  CONCLUSIONS
  Our system paves the ground for fully automated monitoring of the behavior of restrained insects and accounts for individual variations in graded behavior.
authors.affiliation: INCIDE Center (Interdisciplinary Center for Interactive Data Analysis, Modelling and Visual Exploration), University of Konstanz, Germany; School of Software Engineering, South China University of Technology, PR China. Electronic address: minmin.shen@uni-konstanz.de.
authors.forename: Minmin
authors.initials: M
authors.lastname: Shen
authors.affiliation: Institute of Neurobiology, University of Konstanz, Germany. Electronic address: paul.szyszka@uni-konstanz.de.
authors.forename: Paul
authors.initials: P
authors.lastname: Szyszka
authors.affiliation: INCIDE Center (Interdisciplinary Center for Interactive Data Analysis, Modelling and Visual Exploration), University of Konstanz, Germany. Electronic address: oliver.deussen@uni-konstanz.de.
authors.forename: Oliver
authors.initials: O
authors.lastname: Deussen
authors.affiliation: Institute of Neurobiology, University of Konstanz, Germany. Electronic address: giovanni.galizia@uni-konstanz.de.
authors.forename: C Giovanni
authors.initials: CG
authors.lastname: Galizia
authors.affiliation: Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany. Electronic address: Dorit.Merhof@lfb.rwth-aachen.de.
authors.forename: Dorit
authors.initials: D
authors.lastname: Merhof
mesh_terms: D000465:Algorithms; D000818:Animals; D057506:Arthropod Antennae / Q000502:physiology*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001516:Bees / Q000502:physiology*; D003214:Conditioning, Classical; D008297:Male; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D009812:Odorants; D012149:Restraint, Physical; D012984:Software; D014741:Video Recording
keywords: Antenna; Behavior; Classical conditioning; Honey bee; Insect; Multi-target tracking