Tracking multiple ants in a colony

Thomas Fasciano, Hoan Nguyen, Anna Dornhaus, Min C. Shin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


The automated tracking of social insects, such as ants, could dramatically increase the fidelity and amount of analyzed data for studying complex group behaviors. Recently, data association based multiple object tracking methods have shown promise in improving handling of occlusions. However, the tracking of ants in a colony is still challenging as (1) their motion is often sporadic and irregular and (2) they are mostly present the entire duration of video. In this paper, we propose to improve the data association based tracking of multiple ants. First, we model the ant's motion using a set of irregular motion features including random walk model. Second, we use the convergence of particle filter based tracking to match tracklets with a long temporal gap. Testing results of two-fold cross validation on a 10,000 frame video shows that our proposed method was able to reduce the number of fragments by 61% and ID switches by 57%.

Original languageEnglish (US)
Title of host publication2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Number of pages7
StatePublished - 2013
Event2013 IEEE Workshop on Applications of Computer Vision, WACV 2013 - Clearwater Beach, FL, United States
Duration: Jan 15 2013Jan 17 2013

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986


Other2013 IEEE Workshop on Applications of Computer Vision, WACV 2013
Country/TerritoryUnited States
CityClearwater Beach, FL

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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